CLIP-DDPM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EF5yIsqlSN6o"
      },
      "source": [
        "# Download flickr dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ADHTStHPSN6t",
        "outputId": "0baffb4c-f011-4fc8-f64a-6fc0b3249bc8"
      },
      "outputs": [],
      "source": [
        "!wget https://github.com/xu-shitong/flickr8k-CLIP-freature/raw/master/image_all_final.pickle.zip\n",
        "!wget https://github.com/xu-shitong/flickr8k-CLIP-freature/raw/master/text_all_final.pickle.zip\n",
        "!wget https://github.com/xu-shitong/flickr8k-CLIP-freature/raw/master/captions.txt.zip\n",
        "!unzip -q image_all_final.pickle.zip \n",
        "!unzip -q text_all_final.pickle.zip\n",
        "!unzip -q captions.txt.zip\n",
        "!pip install transformers\n",
        "!pip install gputil\n",
        "!pip install psutil\n",
        "!pip install humanize\n",
        "!pip install torchtext\n",
        "!touch summary.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9T9e0CnSN6v"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4x_BGB-lSN6w",
        "outputId": "b7a596a3-70bc-4ade-ee08-80fbf4b31749"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/xushitong/miniconda3/envs/mlenv/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "using device:  cpu\n"
          ]
        }
      ],
      "source": [
        "from PIL import Image\n",
        "import pandas as pd\n",
        "import copy\n",
        "from torchvision.datasets import CocoCaptions\n",
        "from transformers import (\n",
        "  DistilBertTokenizer, DistilBertForMaskedLM, DistilBertConfig,\n",
        "  CLIPProcessor, CLIPModel as CLIP, CLIPConfig,\n",
        "  activations, PreTrainedTokenizer\n",
        ")\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "from torch import nn, optim\n",
        "import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "  dev = \"cuda:0\"\n",
        "else:\n",
        "  dev = \"cpu\"\n",
        "device = torch.device(dev)\n",
        "print(\"using device: \", dev)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZnRNF5VuSN6y",
        "outputId": "46129bdf-6899-48f8-cdb8-f4e395d9911c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU RAM Free: 25.8 GB\n",
            "GPU 0 ... Mem Free: 15106MB / 15109MB | Utilization   0%\n"
          ]
        }
      ],
      "source": [
        "# Import packages\n",
        "import os,sys,humanize,psutil,GPUtil\n",
        "\n",
        "# Define function\n",
        "def mem_report():\n",
        "  print(\"CPU RAM Free: \" + humanize.naturalsize( psutil.virtual_memory().available ))\n",
        "  \n",
        "  GPUs = GPUtil.getGPUs()\n",
        "  for i, gpu in enumerate(GPUs):\n",
        "    print('GPU {:d} ... Mem Free: {:.0f}MB / {:.0f}MB | Utilization {:3.0f}%'.format(i, gpu.memoryFree, gpu.memoryTotal, gpu.memoryUtil*100))\n",
        "\n",
        "mem_report()"
      ]
    },
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      "cell_type": "code",
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "duaPghHXSN6z",
        "outputId": "e137c936-6f3d-48c2-ab3b-0bbf0c436a97"
      },
      "outputs": [],
      "source": [
        "# download pretrained model and tokenizer\n",
        "def save_model_tokenizer(tokenizer_class, model_class, name):\n",
        "  if tokenizer_class is not None:\n",
        "    tokenizer = tokenizer_class.from_pretrained(name)\n",
        "    tokenizer.save_pretrained(f\"./tokenizers/{name}-local\")\n",
        "  if model_class is not None:\n",
        "    model = model_class.from_pretrained(name)\n",
        "    model.save_pretrained(f\"./models/{name}-local/\")\n",
        "\n",
        "# save_model_tokenizer(CLIPProcessor, CLIP, \"openai/clip-vit-base-patch32\")\n",
        "save_model_tokenizer(DistilBertTokenizer, DistilBertForMaskedLM, \"distilbert-base-uncased\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EbYgkaDLSN60"
      },
      "source": [
        "# Hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "gj7o2tSmSN60"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trial name: epoch15_lossseries_sum_sample_mean_lr1E-04-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipconcat_class_weight0E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue\n"
          ]
        }
      ],
      "source": [
        "# hyperparameters\n",
        "DEBUG = False\n",
        "CONTINUE_TRAIN = False\n",
        "BATCH_SIZE = 8\n",
        "MAX_LENGTH = 16 # max text length\n",
        "LEARNING_RATE = 1e-4\n",
        "END_LEARNING_RATE = 5e-5 # learning rate is linearly reduced to end_learning_rate\n",
        "# END_LEARNING_RATE = LEARNING_RATE # no changing learning rate\n",
        "\n",
        "def cosine_annealing():\n",
        "  sub_epoch = 5\n",
        "  x = torch.arange(0, sub_epoch)\n",
        "  x = END_LEARNING_RATE + (LEARNING_RATE - END_LEARNING_RATE) * (1 + torch.cos(x / sub_epoch * math.pi)) / 2\n",
        "  return x.repeat((3, ))\n",
        "# SCHEDULER = torch.logspace\n",
        "SCHEDULER = torch.linspace\n",
        "# SCHEDULER = cosine_annealing # scheduler of learning rate\n",
        "TRAIN_SET_RATIO = 0.95\n",
        "EARLY_STOP_RATIO = 1.02\n",
        "EPOCH_NUM = 15\n",
        "DYNAMIC_ROUNDING_WEIGHT = -1 # weight of rounding term with respect to x_t loss, <0 means not using \n",
        "ROUNDING_WEIGHT = 0.3 # weight of rounding term, the probability of regenerated sequence, not used if using dynamic rounding\n",
        "\n",
        "def series_sum_sample_mean(x_hat, x):\n",
        "  return (x_hat - x).abs().sum(dim=1).mean()\n",
        "\n",
        "def series_sum(x_hat, x):\n",
        "  return (x_hat - x).abs().sum() / BATCH_SIZE / 768 / 100\n",
        "\n",
        "def mse_series_mean(x_hat, x):\n",
        "  return ((x_hat - x) ** 2).sum(dim=[-2, -1]).sqrt().mean()\n",
        "\n",
        "def mse_series_sum(x_hat, x):\n",
        "  return ((x_hat - x) ** 2).sum(dim=[-2, -1]).sqrt().sum() / BATCH_SIZE\n",
        "\n",
        "LOSS_FUNC = series_sum_sample_mean\n",
        "# LOSS_FUNC = series_sum\n",
        "# LOSS_FUNC = mse_series_mean\n",
        "# LOSS_FUNC = mse_series_sum # loss function used between embedding \n",
        "# CLIP_ADDING_METHOD = \"add\" # CLIP feature are added as position embedding to sequence of word embedding\n",
        "CLIP_ADDING_METHOD = \"concat\" # CLIP feature are appended to sequence of word embedding\n",
        "CLASSIFIER_FREE_WEIGHT = 0\n",
        "# CLASSIFIER_FREE_WEIGHT = 1 # classifier guidance, <= 0 means no guidance\n",
        "CLASSIFIER_FREE_PROB = 0.2\n",
        "TRAIN_EMBEDDING = False # if model use pretrained distilbert embedding, or learn a 16 embedding for each word and project to 768 before pass to bert\n",
        "if TRAIN_EMBEDDING:\n",
        "  IN_CHANNEL = 16\n",
        "else:\n",
        "  IN_CHANNEL = 768\n",
        "\n",
        "# diffusion hyperparameter\n",
        "BETA_MIN = 0.0001\n",
        "BETA_MAX = 0.02\n",
        "STEP_TOT = 1000 # total noise adding steps\n",
        "COSIN_SCHEDULE = True # if alpha sequence is scheduled in cosin instead of linear patten\n",
        "SAMPLE_SIZE = 100 # number of sample steps in each diffuse sequence\n",
        "X_0_PREDICTION = True # if model predicts x_0 or x_{t-1}\n",
        "X_T_STEP_INTERVAL = 100\n",
        "USE_X_T_LOSS = True\n",
        "USE_X_1_LOSS = True # if using x_1 loss\n",
        "USE_PROB_LOSS = True # if using prob loss\n",
        "\n",
        "MODEL_NAME = f\"epoch{EPOCH_NUM}_loss{LOSS_FUNC.__name__}_lr{'%.0E' % LEARNING_RATE}-{'%.0E' % END_LEARNING_RATE}_scheduler{SCHEDULER.__name__}_round{'%.0E' % ROUNDING_WEIGHT}_dynamic{DYNAMIC_ROUNDING_WEIGHT}\\\n",
        "_clip{CLIP_ADDING_METHOD}_class_weight{'%.0E' % CLASSIFIER_FREE_WEIGHT}_class_prob{'%.0E' % CLASSIFIER_FREE_PROB}_train-embed{TRAIN_EMBEDDING}\\\n",
        "_samplesize{SAMPLE_SIZE}_x_0_predict{X_0_PREDICTION}_X_INTERVAL{X_T_STEP_INTERVAL}_use_x_t{USE_X_T_LOSS}_use_x_1{USE_X_1_LOSS}_use_prob{USE_PROB_LOSS}\"\n",
        "print(f\"trial name: {MODEL_NAME}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vh2IWxSeSN61"
      },
      "source": [
        "# Define Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "flickr8k_image = torch.load(\"./flickr8k/image_all_final.pickle\").to(device).detach()\n",
        "flickr8k_text = torch.load(\"./flickr8k/text_all_final.pickle\").to(device).detach()\n",
        "flickr30k_image = torch.load(\"./flickr30k/flickr30k_clip_image.pickle\").to(device).detach()\n",
        "flickr30k_text = torch.load(\"./flickr30k/flickr30k_clip_text.pickle\").to(device).detach()\n",
        "image_set = torch.vstack([flickr8k_image, flickr30k_image])\n",
        "text_set = torch.vstack([flickr8k_text, flickr30k_text])\n",
        "# image_set = flickr8k_image\n",
        "# text_set = flickr8k_text\n",
        "\n",
        "from spacy.lang.en import English\n",
        "from collections import Counter\n",
        "import itertools\n",
        "\n",
        "captions = pd.read_csv(\"./flickr8k/captions.txt\")[\"caption\"]\n",
        "nlp = English()\n",
        "\n",
        "sentence_lst = []\n",
        "\n",
        "for sentences in captions:\n",
        "  word_lst = [x.text.lower() for x in nlp.tokenizer(sentences)]\n",
        "  spl = [[]]\n",
        "  for x, y in itertools.groupby(word_lst, lambda z: z == '.'):\n",
        "      spl[-1].extend(y)\n",
        "      if x: spl.append([])\n",
        "  sentence_lst.extend(spl[:-1])\n",
        "\n",
        "counter = Counter()\n",
        "for input_ids in sentence_lst:\n",
        "    counter.update(input_ids)\n",
        "vocab_dict = {'START': 0, 'END': 1, 'UNK':2, 'PAD':3}\n",
        "for k, v in counter.items():\n",
        "    if v > 10:\n",
        "      vocab_dict[k] = len(vocab_dict)\n",
        "\n",
        "class DictTokenizer():\n",
        "  def __init__(self, dictionary) -> None:\n",
        "    self.dictionary = dictionary\n",
        "\n",
        "  def __getitem__(self, i):\n",
        "    return self.dictionary[i]\n",
        "\n",
        "  def decode(self, index):\n",
        "    return \" \".join([list(self.dictionary.keys())[list(self.dictionary.values()).index(i.item())] for i in index])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "b0T_p9ADSN63"
      },
      "outputs": [],
      "source": [
        "\n",
        "class FlickrCLIPDataset(torch.utils.data.Dataset):\n",
        "  def __init__(self, captions, images, tokenizer) -> None:\n",
        "    images.name = \"image\"\n",
        "    captions.name = \"caption\"\n",
        "    self.data = pd.concat([images, captions], axis=1)\n",
        "    self.tokenizer = tokenizer\n",
        "\n",
        "    self.train_dataset = torch.utils.data.TensorDataset(image_set.to(device), text_set.to(device))\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.data)\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    image_clip, text_clip = self.train_dataset[idx]\n",
        "    if isinstance(self.tokenizer, PreTrainedTokenizer):\n",
        "      tokens = self.tokenizer(text=self.data.loc[idx][\"caption\"], return_tensors=\"pt\", padding='max_length', truncation=True, max_length=MAX_LENGTH)\n",
        "    else:\n",
        "      ids = [0] + [vocab_dict.get(x, vocab_dict['UNK']) for x in self.data.loc[idx][\"caption\"][:MAX_LENGTH-2]] + [1] \n",
        "      pad_length = max(0, MAX_LENGTH - len(ids))\n",
        "      tokens = dict()\n",
        "      tokens[\"input_ids\"] = torch.tensor(ids + [vocab_dict['UNK']] * pad_length)\n",
        "      tokens[\"attention_mask\"] = torch.tensor([1] * len(ids) + [0] * pad_length)\n",
        "\n",
        "    return {\n",
        "      \"image_clip\": image_clip, \n",
        "      \"text_clip\": text_clip, \n",
        "      \"input_ids\": tokens[\"input_ids\"].squeeze().to(device), \n",
        "      \"attention_mask\": tokens[\"attention_mask\"].squeeze().to(device),\n",
        "      \"text\": self.data.loc[idx][\"caption\"],\n",
        "      \"image\": self.data.loc[idx][\"image\"]\n",
        "    }\n",
        "\n",
        "# TODO: COCO dataset\n",
        "\n",
        "if TRAIN_EMBEDDING:\n",
        "  tokenizer = DictTokenizer(vocab_dict)\n",
        "  VOCAB_SIZE = len(vocab_dict)\n",
        "else:\n",
        "  tokenizer = DistilBertTokenizer.from_pretrained(\"./tokenizers/distilbert-base-uncased-local/\", local_files_only=True)\n",
        "  VOCAB_SIZE = tokenizer.vocab_size\n",
        "\n",
        "dataset = FlickrCLIPDataset(\n",
        "  # pd.concat([pd.read_csv(\"./flickr8k/captions.txt\")[\"caption\"], pd.read_csv(\"./flickr30k/captions.csv\", sep='|')[\"caption\"]], ignore_index=True),\n",
        "  # pd.concat([pd.read_csv(\"./flickr8k/captions.txt\")[\"image\"], pd.read_csv(\"./flickr30k/captions.csv\", sep='|')[\"image_name\"]], ignore_index=True),\n",
        "  pd.read_csv(\"./flickr8k/captions.txt\")[\"caption\"],\n",
        "  pd.read_csv(\"./flickr8k/captions.txt\")[\"image\"],\n",
        "  tokenizer)\n",
        "if CONTINUE_TRAIN:\n",
        "  val_set = torch.load(f\"{MODEL_NAME}.valset\")\n",
        "  train_set = torch.utils.data.Subset(dataset, list(set(range(len(dataset))) - set(val_set.indices)))\n",
        "else:\n",
        "  train_len = int(len(dataset) * TRAIN_SET_RATIO)\n",
        "  train_set, val_set = torch.utils.data.random_split(dataset, [train_len, len(dataset) - train_len])\n",
        "train_loader = DataLoader(train_set, shuffle=True, batch_size=BATCH_SIZE, drop_last=True)\n",
        "val_loader = DataLoader(val_set, shuffle=False, batch_size=BATCH_SIZE, drop_last=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_b4j3nWDSN63",
        "outputId": "3adb4f73-dc40-49af-c13e-62ccb24379dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU RAM Free: 24.3 GB\n",
            "GPU 0 ... Mem Free: 1543MB / 15109MB | Utilization  90%\n"
          ]
        }
      ],
      "source": [
        "mem_report()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8ZtnOUJSN64"
      },
      "source": [
        "# Model, trainer and loss function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "mJYCiyY8SN64"
      },
      "outputs": [],
      "source": [
        "class DistilBertModel(nn.Module):\n",
        "  def __init__(self, embedding=None, projection=None, config=None) -> None:\n",
        "    '''\n",
        "    inputs:\n",
        "      embedding: clip embedding module\n",
        "      config\n",
        "    '''\n",
        "    super().__init__()\n",
        "\n",
        "    self.model = DistilBertForMaskedLM(config).to(device)\n",
        "\n",
        "    if TRAIN_EMBEDDING:\n",
        "      self.embedding = nn.Embedding(VOCAB_SIZE, IN_CHANNEL, device=device).requires_grad_(True)\n",
        "      self.lm_head = nn.Linear(IN_CHANNEL, VOCAB_SIZE, bias=False, device=device).requires_grad_(True)\n",
        "\n",
        "      self.input_projection = nn.Linear(IN_CHANNEL, 768, device=device).requires_grad_(True)\n",
        "      self.output_projection = nn.Linear(768, IN_CHANNEL, device=device).requires_grad_(True)\n",
        "    else:\n",
        "      self.embedding = copy.deepcopy(embedding.requires_grad_(False))\n",
        "      self.lm_head = copy.deepcopy(projection.requires_grad_(False))\n",
        "      self.lm_head.bias.data = torch.zeros(self.lm_head.bias.data.shape, device=device).requires_grad_(False)\n",
        "    \n",
        "    self.model.set_input_embeddings(nn.Sequential())\n",
        "    self.model.set_output_embeddings(nn.Sequential())\n",
        "\n",
        "    self.image_linear = nn.Linear(512, 768, device=device)\n",
        "    self.text_linear = nn.Linear(512, 768, device=device)\n",
        "\n",
        "    if CLIP_ADDING_METHOD == \"concat\":\n",
        "      self.segment_embedding = nn.Embedding(2, 768, device=device)\n",
        "\n",
        "  def parameters(self):\n",
        "    base_list = list(self.model.parameters()) + list(self.image_linear.parameters()) + list(self.text_linear.parameters())\n",
        "    if TRAIN_EMBEDDING:\n",
        "      base_list += list(self.embedding.parameters()) + list(self.lm_head.parameters()) \\\n",
        "                  + list(self.input_projection.parameters()) + list(self.output_projection.parameters())\n",
        "\n",
        "    if CLIP_ADDING_METHOD == \"concat\":\n",
        "      return base_list + list(self.segment_embedding.parameters())\n",
        "    elif CLIP_ADDING_METHOD == \"add\":\n",
        "      return base_list\n",
        "    else:\n",
        "      raise NotImplementedError(CLIP_ADDING_METHOD)\n",
        "\n",
        "  def forward(self, x, image_clip, text_clip, mask, concat_mask):\n",
        "    '''\n",
        "    input:\n",
        "      x: [x_t ... x_t], shape: [sample_size * batch_size, seq_len, IN_CHANNEL]\n",
        "      image_clip, text_clip shape: [sample_size * batch_size, 1, clip_dim]\n",
        "      mask shape: [sample_size * batch_size, seq_len] \n",
        "    \n",
        "    return \n",
        "      vocab_out, shape: [sample_size * batch_size, seq_len, vocab_size]\n",
        "      feature_out, shape: [sample_size * batch_size, seq_len, IN_CHANNEL]\n",
        "    '''\n",
        "    sample_batch_multi, _, _ = x.shape\n",
        "\n",
        "    assert x.shape == (sample_batch_multi, MAX_LENGTH, IN_CHANNEL)\n",
        "    assert image_clip.shape == text_clip.shape == (sample_batch_multi, 1, 512)\n",
        "    assert mask.shape == (sample_batch_multi, MAX_LENGTH)\n",
        "    assert concat_mask.shape == (sample_batch_multi, 2)\n",
        "\n",
        "    # mask of which sample is classifier free guided, true if guided\n",
        "    guidance_sample_index = (concat_mask[:, 1] == 1)\n",
        "\n",
        "    if TRAIN_EMBEDDING:\n",
        "      x = self.input_projection(x)\n",
        "    \n",
        "    if CLIP_ADDING_METHOD == \"concat\":\n",
        "      classifier_guided_mask = torch.hstack([mask, torch.tensor([1, 1], device=device).repeat(sample_batch_multi, 1)])\n",
        "      non_classifier_mask = torch.hstack([mask, torch.tensor([1, 0], device=device).repeat(sample_batch_multi, 1)])\n",
        "\n",
        "      x = torch.hstack([x, self.image_linear(image_clip), self.text_linear(text_clip)])\n",
        "      x = x + self.segment_embedding(torch.tensor([0] * MAX_LENGTH + [1] * 2, device=device))\n",
        "\n",
        "      classifier_guided_x = non_classifier_x = x\n",
        "    elif CLIP_ADDING_METHOD == \"add\":\n",
        "      classifier_guided_mask = non_classifier_mask = mask\n",
        "\n",
        "      non_classifier_x = x + self.image_linear(image_clip)\n",
        "      classifier_guided_x = non_classifier_x + self.text_linear(text_clip)\n",
        "    else:\n",
        "      raise NotImplementedError(CLIP_ADDING_METHOD)\n",
        "\n",
        "    # no classifier guidance part\n",
        "    x_out = self.model(non_classifier_x, non_classifier_mask)[0]\n",
        "    if CLASSIFIER_FREE_WEIGHT > 0 and not guidance_sample_index.sum() == 0:\n",
        "      # classifier guided\n",
        "      x_out[guidance_sample_index] = \\\n",
        "        (1 + CLASSIFIER_FREE_WEIGHT) * self.model(classifier_guided_x[guidance_sample_index], classifier_guided_mask[guidance_sample_index])[0] \\\n",
        "        - CLASSIFIER_FREE_WEIGHT * x_out[guidance_sample_index]\n",
        "    \n",
        "    if TRAIN_EMBEDDING:\n",
        "      x_out = self.output_projection(x_out)\n",
        "\n",
        "    assert x_out.shape == (sample_batch_multi, non_classifier_mask.shape[-1], IN_CHANNEL)\n",
        "    return self.lm_head(x_out[:, :MAX_LENGTH, :]), x_out\n",
        "    \n",
        "if TRAIN_EMBEDDING:\n",
        "  configuration = DistilBertConfig()\n",
        "  model = DistilBertModel(config=configuration)\n",
        "else:\n",
        "  origin = DistilBertForMaskedLM.from_pretrained(\"./models/distilbert-base-uncased-local\", local_files_only=True).to(device)\n",
        "  configuration = DistilBertConfig()\n",
        "  model = DistilBertModel(origin.get_input_embeddings(), origin.get_output_embeddings(), config=configuration)\n",
        "\n",
        "# parameter only include model, no embedding layer\n",
        "trainer = optim.AdamW(model.parameters(), lr=LEARNING_RATE)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "zUFL-SbASN65"
      },
      "outputs": [],
      "source": [
        "if COSIN_SCHEDULE:\n",
        "  def scheduler(t):\n",
        "    s = 0.008 # smalle value prevent beta_t too small, from Improved DDPM paper\n",
        "    return torch.cos(math.pi / 2 * (t/STEP_TOT + s) / (1 + s)) ** 2\n",
        "  ts = torch.arange(STEP_TOT).to(device)\n",
        "  alpha_cumprod = scheduler(ts) / scheduler(torch.zeros(1, device=device))\n",
        "else:\n",
        "  betas = torch.hstack([torch.zeros(1), torch.linspace(BETA_MIN, BETA_MAX, STEP_TOT)]).to(device)\n",
        "  alphas = 1 - betas\n",
        "  alpha_cumprod = torch.cumprod(alphas[:-1], 0)\n",
        "def diffuse_t(x, t):\n",
        "  '''\n",
        "  input:\n",
        "    x_shape: [batch_size, seq_len, IN_CHANNEL]\n",
        "    t shape: [sample num] \n",
        "      NOTE: not necessary have hyperparameter sample_size number of element, to allow single diffuse generation\n",
        "\n",
        "  return shape [sample_num * batch_size, seq_len, IN_CHANNEL]\n",
        "  '''\n",
        "  batch_size, seq_len, _ = x.shape\n",
        "  sample_shape = (t.numel(), *(1, ) * len(x.shape))\n",
        "\n",
        "  noise = torch.normal(0, 1, x.shape).to(device)\n",
        "  mean = torch.sqrt(alpha_cumprod[t].reshape(sample_shape)) * x \n",
        "  epsilon = noise * torch.sqrt(1 - alpha_cumprod[t]).reshape(sample_shape)\n",
        "  return (mean + epsilon).reshape((t.numel() * batch_size, seq_len, IN_CHANNEL))\n",
        "\n",
        "def generate_diffuse_pair(x_0, t, t_next=None):\n",
        "  '''\n",
        "  input:\n",
        "    x_0 shape: [batch_size, seq_len, IN_CHANNEL],\n",
        "    t shape: [sample_num] \n",
        "      NOTE: not necessary have hyperparameter sample_size number of element, to allow single diffuse generation\n",
        "  \n",
        "  return (net input, net target)\n",
        "    net input shape: [sample_num * batch_size, seq_len, IN_CHANNEL]\n",
        "    net target shape: if t_next is None then [batch_size, seq_len, IN_CHANNEL] else [sample_num * batch_size, seq_len, IN_CHANNEL]\n",
        "  '''\n",
        "  if X_0_PREDICTION:\n",
        "    # predict x_0\n",
        "    return (diffuse_t(x_0, t), x_0)\n",
        "\n",
        "  # predict x_{t_next}\n",
        "  return (diffuse_t(x_0, t), diffuse_t(x_0, t_next))\n",
        "\n",
        "def loss(model, x_t, x_1, x_tgt, x_0, image_clip, text_clip, mask, idx, loss_func):\n",
        "  ''' \n",
        "  input: \n",
        "    model, \n",
        "    x_t, x_tgt shape: [sample_num * batch_size, seq_len, IN_CHANNEL]\n",
        "      NOTE: x_tgt only used when X_0_PREDICTION is False\n",
        "    x_1, x_0 shape: [batch_size, seq_len, IN_CHANNEL]\n",
        "    image_clip, text_clip shape: [batch_size, clip_dim]\n",
        "    mask shape: [batch_size, seq_len]\n",
        "    idx shape: [batch_size, seq_len]\n",
        "    loss_func\n",
        "\n",
        "  return triple loss terms\n",
        "  '''\n",
        "  assert x_t.shape == (SAMPLE_SIZE * BATCH_SIZE, MAX_LENGTH, IN_CHANNEL)\n",
        "  assert x_1.shape == x_0.shape == (BATCH_SIZE, MAX_LENGTH, IN_CHANNEL)\n",
        "  assert image_clip.shape == text_clip.shape == (BATCH_SIZE, 512)\n",
        "  assert mask.shape == (BATCH_SIZE, MAX_LENGTH)\n",
        "  assert idx.shape == (BATCH_SIZE, MAX_LENGTH)\n",
        "  \n",
        "  repeat_shape = (SAMPLE_SIZE, *(1, ) * (len(x_t.shape) - 1))\n",
        "  image_clip = image_clip.unsqueeze(1) # shape [ batch_size, 1, clip_dim]\n",
        "  text_clip = text_clip.unsqueeze(1) # shape same as above\n",
        "\n",
        "  if CLASSIFIER_FREE_WEIGHT > 0:\n",
        "    classifier_mask = (torch.rand((SAMPLE_SIZE * BATCH_SIZE, 1)) > CLASSIFIER_FREE_PROB).type(torch.float32).to(device)\n",
        "    classifier_mask[0] = 0\n",
        "    classifier_mask[1] = 1 # prevent no sample or all sample use classifier\n",
        "    concat_mask = torch.hstack([torch.ones((SAMPLE_SIZE * BATCH_SIZE, 1), device=device), classifier_mask])\n",
        "  else:\n",
        "    concat_mask = torch.tensor([1, 0], device=device).repeat((SAMPLE_SIZE * BATCH_SIZE, 1))\n",
        "\n",
        "  # x_t restore loss\n",
        "  x_t_prob, x_t_hidden = model(x_t, image_clip.repeat(repeat_shape), text_clip.repeat(repeat_shape), mask.repeat((SAMPLE_SIZE, 1)), concat_mask)\n",
        "  if USE_X_T_LOSS:\n",
        "    if X_0_PREDICTION:\n",
        "      x_t_loss = loss_func(x_t_hidden[:, :MAX_LENGTH, :], x_0.repeat(repeat_shape))\n",
        "    else:\n",
        "      assert x_tgt.shape == x_t.shape\n",
        "      x_t_loss = loss_func(x_t_hidden[:, :MAX_LENGTH, :], x_tgt)\n",
        "  else:\n",
        "    x_t_loss = 0\n",
        "\n",
        "  # x_1 restore loss\n",
        "  x_1_prob, x_1_hidden = model(x_1, image_clip, text_clip, mask, torch.tensor([1, 0], device=device).repeat((BATCH_SIZE, 1)))\n",
        "  if USE_X_1_LOSS:\n",
        "    x_1_loss = loss_func(x_1_hidden[:, :MAX_LENGTH, :], x_0)\n",
        "  else:\n",
        "    x_1_loss = 0\n",
        "\n",
        "  if USE_PROB_LOSS:\n",
        "    # output sequence probability loss, applied to both x_1 and x_t restore\n",
        "    idx = idx.unsqueeze(dim=-1)\n",
        "    if LOSS_FUNC == series_sum_sample_mean or LOSS_FUNC == mse_series_mean:\n",
        "      x_t_prob_loss = -(nn.functional.softmax(x_t_prob, dim=-1)).gather(-1, idx.repeat(repeat_shape)).log().sum(dim=1).mean()\n",
        "      x_1_prob_loss = -(nn.functional.softmax(x_1_prob, dim=-1)).gather(-1, idx).log().sum(dim=1).mean()\n",
        "    else:\n",
        "      x_t_prob_loss = -(nn.functional.softmax(x_t_prob, dim=-1)).gather(-1, idx.repeat(repeat_shape)).log().sum() / BATCH_SIZE\n",
        "      x_1_prob_loss = -(nn.functional.softmax(x_1_prob, dim=-1)).gather(-1, idx).log().sum() / BATCH_SIZE\n",
        "  else:\n",
        "    x_t_prob_loss = 0\n",
        "    x_1_prob_loss = 0\n",
        "  \n",
        "  return x_t_loss, x_1_loss, ROUNDING_WEIGHT * (x_t_prob_loss + x_1_prob_loss)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pEEKLtr8SN65",
        "outputId": "bdb969df-d60c-424b-cdfe-d50fc773215e"
      },
      "outputs": [],
      "source": [
        "mem_report()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGkYKwDSSN66"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 183,
      "metadata": {},
      "outputs": [],
      "source": [
        "if SCHEDULER == torch.linspace:\n",
        "  lrs = SCHEDULER(LEARNING_RATE, END_LEARNING_RATE, EPOCH_NUM)\n",
        "elif SCHEDULER == torch.logspace:\n",
        "  lrs = SCHEDULER(torch.tensor([LEARNING_RATE]).log10().item(), torch.tensor([END_LEARNING_RATE]).log10().item(), EPOCH_NUM)\n",
        "elif SCHEDULER == cosine_annealing:\n",
        "  lrs = SCHEDULER()\n",
        "\n",
        "def train_func(model, trainer, x, train=True):\n",
        "  x_0 = model.embedding(x[\"input_ids\"])\n",
        "  repeat_shape = (SAMPLE_SIZE, *(1, ) * (len(x_0.shape) - 1))\n",
        "  t = torch.randint(0, STEP_TOT, repeat_shape, device=device)\n",
        "  \n",
        "  if X_0_PREDICTION:\n",
        "    x_t = diffuse_t(x_0, t)\n",
        "    x_tgt = None\n",
        "  else:\n",
        "    x_t, x_tgt = generate_diffuse_pair(x_0, t, torch.max(t - X_T_STEP_INTERVAL, torch.zeros(t.shape, device=device, dtype=torch.int64)))\n",
        "  x_1 = diffuse_t(x_0, torch.ones(1, dtype=torch.int64, device=device))\n",
        "\n",
        "  if train:\n",
        "    trainer.zero_grad()\n",
        "  x_t_loss, x_1_loss, prob_loss = loss(\n",
        "    model, \n",
        "    x_t, x_1, x_tgt, x_0, \n",
        "    x[\"image_clip\"], x[\"text_clip\"], \n",
        "    x[\"attention_mask\"], \n",
        "    x[\"input_ids\"], \n",
        "    LOSS_FUNC\n",
        "  )\n",
        "  \n",
        "  l = x_t_loss + x_1_loss + prob_loss\n",
        "  if train:\n",
        "    l.backward()\n",
        "    trainer.step()\n",
        "\n",
        "  return l, x_t_loss, x_1_loss, prob_loss\n",
        "\n",
        "def validate(model):\n",
        "  val_acc_x_t = 0\n",
        "  val_acc_x_1 = 0\n",
        "  val_acc_prob = 0\n",
        "  model.eval()\n",
        "  with torch.no_grad():\n",
        "    for batch_num, x in enumerate(val_loader):\n",
        "      _, x_t_loss, x_1_loss, prob_loss = train_func(model, trainer, x, train=False)\n",
        "      val_acc_x_t += x_t_loss\n",
        "      val_acc_x_1 += x_1_loss\n",
        "      val_acc_prob += prob_loss\n",
        "  model.train()\n",
        "\n",
        "  return val_acc_x_t / len(val_loader), val_acc_x_1 / len(val_loader), val_acc_prob / len(val_loader), "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 186,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "start training\n",
            "torch.Size([800, 16, 1])\n"
          ]
        }
      ],
      "source": [
        "# training \n",
        "\n",
        "if CONTINUE_TRAIN:\n",
        "  model = torch.load(f\"{MODEL_NAME}.pickle\").to(device)\n",
        "  # model.model.add_module(\"activation\", activations.GELUActivation())\n",
        "  trainer = optim.AdamW(model.parameters(), lr=LEARNING_RATE)\n",
        "# summary = open(f\"{MODEL_NAME}.txt\", \"a\")\n",
        "summary = sys.stdout\n",
        "\n",
        "early_stopped = False\n",
        "model.train()\n",
        "print(\"start training\")\n",
        "for epoch in range(EPOCH_NUM):\n",
        "  acc_x_t = 0\n",
        "  acc_x_1 = 0\n",
        "  acc_prob = 0\n",
        "  acc_l = 0\n",
        "  if not END_LEARNING_RATE == LEARNING_RATE:\n",
        "    for g in trainer.param_groups:\n",
        "      g['lr'] = lrs[epoch]\n",
        "\n",
        "  # with tqdm.tqdm(train_loader, unit=\"batch\") as tepoch: \n",
        "  #   for batch_num, x in enumerate(tepoch):\n",
        "  for batch_num, x in enumerate(train_loader):\n",
        "\n",
        "      l, x_t_loss, x_1_loss, prob_loss = train_func(model, trainer, x)\n",
        "      \n",
        "      acc_x_t += x_t_loss\n",
        "      acc_x_1 += x_1_loss\n",
        "      acc_prob += prob_loss\n",
        "      acc_l += l\n",
        "\n",
        "      if DYNAMIC_ROUNDING_WEIGHT > 0:\n",
        "        ROUNDING_WEIGHT = ((acc_x_t + acc_x_1) / acc_prob).detach() * DYNAMIC_ROUNDING_WEIGHT\n",
        "\n",
        "      # tepoch.set_description(f\"batch {batch_num}\")\n",
        "      # tepoch.set_postfix(\n",
        "      #                    x_t_hidden=x_t_loss.item(),\n",
        "      #                    x_1_loss=x_1_loss.item(),\n",
        "      #                    prob_loss=prob_loss.item(),\n",
        "      #                    tot_loss=l.item())\n",
        "      if DEBUG:\n",
        "        break\n",
        "\n",
        "  val_x_t, val_x_1, val_prob = validate(model)\n",
        "  if val_x_t + val_x_1 + val_prob > EARLY_STOP_RATIO * acc_l / len(train_loader):\n",
        "    if not early_stopped:\n",
        "      summary.write(\"early stop! \\n\")\n",
        "      torch.save(model.cpu(), f\"{MODEL_NAME}.pickle\")\n",
        "      model = model.to(device)\n",
        "    early_stopped = True\n",
        "  summary.write(f\"epoch {epoch} average x_t_loss, x_1_loss, prob_loss, val losses: {acc_x_t / len(train_loader)}, {acc_x_1 / len(train_loader)}, {acc_prob / len(train_loader)}, {val_x_t}, {val_x_1}, {val_prob}\\n\")\n",
        "    \n",
        "  if DEBUG:\n",
        "    break\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "if not early_stopped:\n",
        "  torch.save(model.cpu(), f\"{MODEL_NAME}.pickle\")\n",
        "  model = model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "mem_report()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsDvojUfSN67"
      },
      "source": [
        "# Evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "summary = sys.stdout"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "jqroLU4FSN67",
        "outputId": "9864fb35-d957-470e-88f2-c758db7756e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "origin text: A hockey play shooting for a goal in a professional game .\n",
            "t = 999\n",
            "inferred: [CLS] a woman is walking a street past a a winter night. [SEP] [PAD] [PAD]\n",
            "inferred: [CLS] a woman is walking the street street a a winter night. [SEP] [PAD] [PAD]\n",
            "inferred: [CLS] a woman is walking a the street a a winter night. [SEP] [PAD] [PAD]\n",
            "inferred: [CLS] a woman is walking a the street a a winter night. [SEP] [PAD] [PAD]\n",
            "inferred: [CLS] a woman is walking down the street a a winter night. [SEP] [PAD] [PAD]\n",
            "inferred: [CLS] a woman is walking down the street a a winter night. [SEP] [PAD] [PAD]\n",
            "inferred: [CLS] a woman is walking down the street in a winter night. [SEP] [PAD] [PAD]\n",
            "inferred: [CLS] a woman is walking down the street in a winter winter. [SEP] [PAD] [PAD]\n",
            "inferred: [CLS] a woman is walking down the street outside a winter winter. [SEP] [PAD] [PAD]\n",
            "inferred: [CLS] a woman is walking down the street near a winter winter. [SEP] [PAD] [PAD]\n",
            "text t effectiveness\n",
            "t: 1 restore: [CLS] a hockey play shooting for a goal in a professional game. [SEP] [PAD] [PAD]\n",
            "t: 101 restore: [CLS] a hockey play shooting for a goal in a board game. [SEP] [PAD] [PAD]\n",
            "t: 201 restore: [CLS] a woman is walking towards a street in a city scene. [SEP] [PAD] [PAD]\n",
            "t: 301 restore: [CLS] a dog boy is a the street in a city street. [SEP] [PAD] [PAD]\n",
            "t: 401 restore: [CLS] a woman is walking past a street in a street street. [SEP] [PAD] [PAD]\n",
            "t: 501 restore: [CLS] a woman is outside a a street a a street night. [SEP] [PAD] [PAD]\n",
            "t: 601 restore: [CLS] a woman is walking a street street a a winter night. [SEP] [PAD] [PAD]\n",
            "t: 701 restore: [CLS] a woman is walking a street street a a winter night. [SEP] [PAD] [PAD]\n",
            "t: 801 restore: [CLS] a man is walking a a street a a a night. [SEP] [PAD] [PAD]\n",
            "t: 901 restore: [CLS] a woman is walking a a street a a winter night. [SEP] [PAD] [PAD]\n"
          ]
        }
      ],
      "source": [
        "# trial on inference\n",
        "model = torch.load(\n",
        "  # \"epoch15_lossseries_sum_sample_mean_lr5E-05-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipconcat_class_weight0E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.pickle\"\n",
        "  \"epoch15_lossseries_sum_sample_mean_lr1E-04-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipconcat_class_weight0E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.pickle\"\n",
        "\n",
        "  ).to(device)\n",
        "model.model.add_module(\"activation\", activations.GELUActivation())\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "\n",
        "  idx = 0\n",
        "  summary.write(f\"origin text: {val_set[idx]['text']}\\n\")\n",
        "\n",
        "  sample = val_set[idx]\n",
        "  image_clip = sample[\"image_clip\"][None, None, :]\n",
        "  text_clip = sample[\"text_clip\"][None, None, :]\n",
        "\n",
        "  x_0 = model.embedding(sample[\"input_ids\"].unsqueeze(0))\n",
        "  t = 999\n",
        "  summary.write(f\"t = {t}\\n\")\n",
        "  x_t = diffuse_t(x_0, torch.tensor([t], dtype=torch.int64, device=device))\n",
        "  mask = sample[\"attention_mask\"].unsqueeze(0)\n",
        "\n",
        "  # multi-step inference\n",
        "  restored = x_t\n",
        "  for i in range(10):\n",
        "    out, restored = model(restored[:, :MAX_LENGTH, :], image_clip, text_clip, mask, torch.tensor([1, 0], device=device).repeat(mask.shape[0], 1))\n",
        "    summary.write(f\"inferred: {dataset.tokenizer.decode(out.argmax(dim=-1)[0])}\\n\")\n",
        "\n",
        "  # effectiveness of model on large t\n",
        "  summary.write(\"text t effectiveness\\n\")\n",
        "  for i in range(1, STEP_TOT, 100):\n",
        "    x_t = diffuse_t(x_0, torch.tensor([i], dtype=torch.int64, device=device))\n",
        "    out, _ = model(x_t, image_clip, text_clip, mask, torch.tensor([1, 0], device=device).repeat(mask.shape[0], 1)) \n",
        "\n",
        "    summary.write(f\"t: {i} restore: {dataset.tokenizer.decode(out.argmax(dim=-1)[0])}\\n\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "inferred:  [CLS] a young in in a a in a a a in a a. [SEP]\n",
            "inferred:  [CLS] a woman in a black dress is standing in standing in the background. [SEP]\n",
            "inferred:  [CLS] a woman in a black coat is standing in front in the background. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of the crowd. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of the crowd. [SEP]\n",
            "\n",
            "inferred:  [CLS] a young in in a a and a a a and a a. [SEP]\n",
            "inferred:  [CLS] a woman girl a blue shirt is standing in front in the background. [SEP]\n",
            "inferred:  [CLS] a woman in a blue coat is standing in front of the background. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of the crowd. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of a crowd. [SEP]\n",
            "\n",
            "inferred:  [CLS] a young in a a a a a a a in a a. [SEP]\n",
            "inferred:  [CLS] a woman in a blue shirt and a a standing in the background. [SEP]\n",
            "inferred:  [CLS] a woman in a white coat standing a white flag in the background. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is holding white flag in the background. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is holding a flag in the background. [SEP]\n",
            "\n",
            "inferred:  [CLS] a young in in a in a a a and a a the. [SEP]\n",
            "inferred:  [CLS] a woman in a black coat and white dress standing in the air. [SEP]\n",
            "inferred:  [CLS] a woman in a black coat and white dress standing in the air. [SEP]\n",
            "inferred:  [CLS] a girl in a black coat and white pants standing in the air. [SEP]\n",
            "inferred:  [CLS] a girl in a black coat and white pants standing in the air. [SEP]\n",
            "\n",
            "inferred:  [CLS] a young in in a in a a a a and a a. [SEP]\n",
            "inferred:  [CLS] a woman in a blue dress and white dress standing in the air. [SEP]\n",
            "inferred:  [CLS] a girl in a black coat and white dress standing in the air. [SEP]\n",
            "inferred:  [CLS] a girl in a black coat and white dress standing in the air. [SEP]\n",
            "inferred:  [CLS] a girl in a black coat and white pants standing in the air. [SEP]\n",
            "\n",
            "inferred:  [CLS] a young in a a a and a a a in a the. [SEP]\n",
            "inferred:  [CLS] a woman in a blue dress and standing in front in the background. [SEP]\n",
            "inferred:  [CLS] a woman in a blue coat is standing in front of the background. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of the crowd. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of a crowd. [SEP]\n",
            "\n",
            "inferred:  [CLS] a young in a a a and a a a in a a. [SEP]\n",
            "inferred:  [CLS] a woman in a black dress and standing in standing in the background. [SEP]\n",
            "inferred:  [CLS] a woman in a black coat is standing in front in the background. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of the background. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of the crowd. [SEP]\n",
            "\n",
            "inferred:  [CLS] a young in a a a and a a a in a a. [SEP]\n",
            "inferred:  [CLS] a young girl in a white and standing in front in the background. [SEP]\n",
            "inferred:  [CLS] a woman in a blue dress is standing in front of the background. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of the crowd. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of a crowd. [SEP]\n",
            "\n",
            "inferred:  [CLS] a young girl a a a and a a a in a the. [SEP]\n",
            "inferred:  [CLS] a young girl a a white is standing in front in the background. [SEP]\n",
            "inferred:  [CLS] a woman in a blue dress is standing in front in the background. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of the crowd. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of the crowd. [SEP]\n",
            "\n",
            "inferred:  [CLS] a young in in a a and a a a in a a. [SEP]\n",
            "inferred:  [CLS] a young girl a black dress is in a front in the background. [SEP]\n",
            "inferred:  [CLS] a woman in a white coat is standing in front in the background. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of the background. [SEP]\n",
            "inferred:  [CLS] a girl in a white coat is standing in front of the crowd. [SEP]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Zero shot trial\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "  image = Image.open(\"./Shropshire.jpg\")\n",
        "  clip_processor = CLIPProcessor.from_pretrained(\"./tokenizers/openai/clip-vit-base-patch32-local\")\n",
        "  clip = CLIP.from_pretrained(\"./models/openai/clip-vit-base-patch32-local\")\n",
        "  plt.imshow(image)\n",
        "  plt.show()\n",
        "  inputs = clip_processor(text=\"\", images=image, return_tensors=\"pt\", padding=True)\n",
        "\n",
        "  image_clip = clip.get_image_features(pixel_values=inputs[\"pixel_values\"]).unsqueeze(0)\n",
        "  image_clip = image_clip / image_clip.norm(p=2, dim=-1, keepdim=True)\n",
        "\n",
        "  text_clip = torch.zeros_like(image_clip)\n",
        "\n",
        "  # multi-step inference\n",
        "  for _ in range(10):\n",
        "    restored = torch.randn((1, 18, 768), device=device)\n",
        "    for i in range(10):\n",
        "      \n",
        "      out, restored = model(restored[:, :MAX_LENGTH, :], image_clip, text_clip, torch.ones((1, MAX_LENGTH)), torch.tensor([[1, 0]]))\n",
        "\n",
        "      if i % 2 == 0:\n",
        "        print(\"inferred: \", dataset.tokenizer.decode(nn.functional.softmax(out, dim=-1).argmax(dim=-1)[0]))\n",
        "    print()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Metrics Evaluations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x216 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x216 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x216 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x216 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x720 with 8 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot train and validation error\n",
        "\n",
        "def extract_float(file_name, start=\"\"):\n",
        "  def isfloat(word):\n",
        "    try:\n",
        "      float(word)\n",
        "      return True\n",
        "    except ValueError:\n",
        "      return False\n",
        "  import re\n",
        "  file = open(file_name)\n",
        "  l = [[float(word) for word in re.split(\"\\n|,| \", line) if isfloat(word)] for line in file if line.startswith(start)]\n",
        "  return torch.tensor(l)\n",
        "\n",
        "small_baseline = \"./trial_add_concat/epoch15_lossseries_sum_lr5E-05-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipconcat_clipmask10_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_1True_use_probTrue.txt\"\n",
        "\n",
        "\n",
        "file_names = [ # add or concat\n",
        "  \"./trial_add_concat/epoch15_lossseries_sum_sample_mean_lr5E-05-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipadd_class_weight0E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "  small_baseline,\n",
        "  \"./trial_add_concat/epoch30_lossseries_sum_sample_mean_lr5E-05-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipadd_class_weight0E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "  \"epoch15_lossseries_sum_sample_mean_lr5E-05-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipconcat_class_weight0E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "]\n",
        "fig, axarr = plt.subplots(1, 5, figsize=(20, 3))\n",
        "fig.tight_layout(pad=2.0)\n",
        "names = [\"addition & Flickr8k\", \"concat & Flickr8k\", \"addition & Flickr30+8k\", \"concat & Flickr30+8k\"]\n",
        "axarr[4].set_title(\"validation L_diffuseLM\")\n",
        "for i, file_name in enumerate(file_names):\n",
        "  losses = extract_float(file_name, start=\"epoch\")[:20] # shape [batch size, 7] each line is [batch number, x_t, x_1, prob losses, validation x_t x_1 prob losses]\n",
        "  axarr[i].set_title(names[i])\n",
        "  axarr[i].plot(losses[:, 1], color='r', label=\"train loss\")\n",
        "  axarr[i].plot(losses[:, 4], color='b', label=\"validation loss\")\n",
        "  axarr[4].plot(losses[:, 4] + losses[:, 6], label=names[i])\n",
        "  axarr[i].legend(loc=\"upper right\")\n",
        "  axarr[4].legend(loc=\"upper right\")\n",
        "  axarr[i].set_xlabel(\"epoch\")\n",
        "  axarr[i].set_ylabel(\"L'_simple\")\n",
        "axarr[4].set_xlabel(\"epoch\")\n",
        "axarr[4].set_ylabel(\"L_diffuseLM\")\n",
        "\n",
        "\n",
        "file_names = [ # rounding weight\n",
        "  \"./trial_rounding_weight/epoch15_lossseries_sum_sample_mean_lr5E-05-5E-05_schedulerlogspace_round1E+00_dynamic-1_clipconcat_clipmask10_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "  \"./trial_rounding_weight/epoch15_lossseries_sum_sample_mean_lr5E-05-5E-05_schedulerlogspace_round5E-01_dynamic-1_clipconcat_clipmask10_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "  small_baseline,\n",
        "  \"./trial_rounding_weight/epoch15_lossseries_sum_sample_mean_lr5E-05-5E-05_schedulerlogspace_round2E-01_dynamic-1_clipconcat_clipmask10_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "]\n",
        "fig, axarr = plt.subplots(1, 5, figsize=(20, 3))\n",
        "fig.tight_layout(pad=2.0)\n",
        "names = [\"lambda = 1.0\", \"lambda = 0.5\", \"lambda = 0.3\", \"lambda = 0.2\"]\n",
        "lambdas = [1.0, 0.5, 0.3, 0.2]\n",
        "axarr[4].set_title(\"L_R\")\n",
        "for i, file_name in enumerate(file_names):\n",
        "  losses = extract_float(file_name, start=\"epoch\") # shape [batch size, 7] each line is [batch number, x_t, x_1, prob losses, validation x_t x_1 prob losses]\n",
        "  axarr[i].set_title(names[i])\n",
        "  axarr[i].plot(losses[:, 1], color='r', label=\"train loss\")\n",
        "  axarr[i].plot(losses[:, 4], color='b', label=\"validation loss\")\n",
        "  axarr[i].legend(loc=\"upper right\")\n",
        "  axarr[4].plot(losses[:, 3] / lambdas[i], label=names[i])\n",
        "  axarr[i].set_xlabel(\"epoch\")\n",
        "  axarr[i].set_ylabel(\"L'_simple\")\n",
        "axarr[4].legend(loc=\"upper right\")\n",
        "axarr[4].set_xlabel(\"epoch\")\n",
        "axarr[4].set_ylabel(\"L_R\")\n",
        "\n",
        "\n",
        "file_names = [ # dynamic rounding weight\n",
        "  small_baseline,\n",
        "  \"./trial_rounding_weight/epoch15_lossseries_sum_sample_mean_lr1E-04-1E-04_schedulerlinspace_round3E-01_dynamic1_clipconcat_class_weight-1E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "  \"./trial_rounding_weight/epoch15_lossseries_sum_sample_mean_lr5E-05-5E-05_schedulerlinspace_round3E+00_dynamic3_clipconcat_clipmask10_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "]\n",
        "fig, axarr = plt.subplots(1, 4, figsize=(20, 3))\n",
        "fig.tight_layout(pad=2.0)\n",
        "names = [\"lambda = 0.3 const\", \"C = 1\", \"C = 3\"]\n",
        "axarr[3].set_title(\"L_R\")\n",
        "for i, file_name in enumerate(file_names):\n",
        "  losses = extract_float(file_name, start=\"epoch\") # shape [batch size, 7] each line is [batch number, x_t, x_1, prob losses, validation x_t x_1 prob losses]\n",
        "  axarr[i].set_title(names[i])\n",
        "  axarr[i].plot(losses[:, 1], color='r', label=\"train loss\")\n",
        "  axarr[i].plot(losses[:, 4], color='b', label=\"val loss\")\n",
        "  axarr[i].legend(loc=\"upper right\")\n",
        "  axarr[i].set_xlabel(\"epoch\")\n",
        "  axarr[i].set_ylabel(\"L'_simple\")\n",
        "  axarr[3].plot(losses[:, 3], label=names[i])\n",
        "axarr[3].legend(loc=\"upper right\")\n",
        "axarr[3].set_xlabel(\"epoch\")\n",
        "axarr[3].set_ylabel(\"L_R\")\n",
        "\n",
        "\n",
        "file_names = [ # classifier free trial\n",
        "  small_baseline,\n",
        "  \"./trial_classification_free/epoch15_lossseries_sum_sample_mean_lr5E-05-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipconcat_class_weight1E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "  \"./trial_classification_free/epoch15_lossseries_sum_sample_mean_lr5E-05-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipconcat_class_weight3E-01_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "]\n",
        "\n",
        "file_names = [ # const learning rate !!\n",
        "  \"./trial_lr/epoch15_lossseries_sum_sample_mean_lr5E-04-5E-04_schedulerlinspace_round3E-01_dynamic-1_clipconcat_clipmask10_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "  \"./trial_lr/epoch15_lossseries_sum_sample_mean_lr1E-04-1E-04_schedulerlogspace_round3E-01_dynamic-1_clipconcat_clipmask10_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "  small_baseline,\n",
        "  \"./trial_lr/epoch15_lossseries_sum_sample_mean_lr1E-05-1E-05_schedulerlogspace_round3E-01_dynamic-1_clipconcat_clipmask10_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "]\n",
        "fig, axarr = plt.subplots(1, 5, figsize=(20, 3))\n",
        "fig.tight_layout(pad=2.0)\n",
        "names = [\"lr = 5e-4\", \"lr = 1e-4\", \"lr = 5e-5\", \"lr = 1e-5\"]\n",
        "axarr[4].set_title(\"L_R\")\n",
        "for i, file_name in enumerate(file_names):\n",
        "  losses = extract_float(file_name, start=\"epoch\") # shape [batch size, 7] each line is [batch number, x_t, x_1, prob losses, validation x_t x_1 prob losses]\n",
        "  axarr[i].set_title(names[i])\n",
        "  axarr[i].plot(losses[:, 1], color='r', label=\"train loss\")\n",
        "  axarr[i].plot(losses[:, 4], color='b', label=\"val loss\")\n",
        "  axarr[i].legend(loc=\"upper right\")\n",
        "  axarr[4].plot(losses[:, 3], label=names[i])\n",
        "  axarr[i].set_xlabel(\"epoch\")\n",
        "  axarr[i].set_ylabel(\"L'_simple\")\n",
        "axarr[4].legend(loc=\"upper right\")\n",
        "axarr[4].set_xlabel(\"epoch\")\n",
        "axarr[4].set_ylabel(\"L_R\")\n",
        "\n",
        "\n",
        "file_names = [ # learning rate scheduling\n",
        "  small_baseline,  \n",
        "  \"./trial_lr/epoch15_lossseries_sum_sample_mean_lr1E-04-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipconcat_class_weight0E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "  \"./trial_lr/epoch15_lossseries_sum_sample_mean_lr1E-04-5E-05_schedulercosine_annealing_round3E-01_dynamic-1_clipconcat_class_weight0E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "  \"./trial_lr/epoch15_lossseries_sum_sample_mean_lr1E-04-5E-05_schedulerlogspace_round3E-01_dynamic-1_clipconcat_class_weight0E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "]\n",
        "\n",
        "# file_names = [ # x_0 or x_t trial\n",
        "#   \"./trial_x_01_prediction/epoch15_lossseries_sum_sample_mean_lr5E-05-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipconcat_class_weight0E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictFalse_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "#   small_baseline,\n",
        "# ]\n",
        "\n",
        "\n",
        "# general propose plot\n",
        "file_names = [ # final model 0.5 or 0.3 plot\n",
        "  \"./epoch15_lossseries_sum_sample_mean_lr5E-05-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipconcat_class_weight0E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "  \"./epoch15_lossseries_sum_sample_mean_lr1E-04-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipconcat_class_weight0E+00_class_prob2E-01_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_tTrue_use_x_1True_use_probTrue.txt\",\n",
        "]\n",
        "fig, axarr = plt.subplots(len(file_names), 4, figsize=(15, 10))\n",
        "for i, file_name in enumerate(file_names):\n",
        "  losses = extract_float(file_name, start=\"epoch\") # shape [batch size, 7] each line is [batch number, x_t, x_1, prob losses, validation x_t x_1 prob losses]\n",
        "  train_index = {1: \"x_t_loss\",2: \"x_1_loss\",3: \"prob_loss\"}\n",
        "  val_index = {4: \"x_t_loss\",5: \"x_1_loss\",6: \"prob_loss\"}\n",
        "  train_sum_losses = losses[:, list(train_index.keys())].sum(dim=-1)\n",
        "  val_sum_losses = losses[:, list(val_index.keys())].sum(dim=-1)\n",
        "\n",
        "  for j, idx in enumerate(train_index):\n",
        "    axarr[i, j].plot(losses[:, idx])\n",
        "    axarr[i, j].set_title(train_index[idx])\n",
        "\n",
        "  for j, idx in enumerate(val_index):\n",
        "    axarr[i, j].plot(losses[:, idx])\n",
        "    axarr[i, j].set_title(val_index[idx])\n",
        "\n",
        "  axarr[i, 3].plot(train_sum_losses)\n",
        "  axarr[i, 3].plot(val_sum_losses)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " 40%|███▉      | 100/252 [02:03<03:07,  1.23s/batch]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor(0.1735)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "from torchmetrics import BLEUScore\n",
        "\n",
        "# model = torch.load(\n",
        "#   \"./trial_add_concat/epoch15_lossseries_sum_lr5E-05-5E-05_schedulerlinspace_round3E-01_dynamic-1_clipconcat_clipmask10_train-embedFalse_samplesize100_x_0_predictTrue_X_INTERVAL100_use_x_1True_use_probTrue.pickle\"\n",
        "#   ).to(device)\n",
        "# model.model.add_module(\"activation\", activations.GELUActivation())\n",
        "model.eval()\n",
        "metric = BLEUScore()\n",
        "acc_bleu = 0\n",
        "with torch.no_grad():\n",
        "  # with tqdm.tqdm(val_loader, unit=\"batch\") as tepoch: \n",
        "  #   for j, x in enumerate(tepoch):\n",
        "    for j, x in enumerate(val_loader):\n",
        "\n",
        "      restored = torch.randn((x[\"input_ids\"].shape[0], MAX_LENGTH + 2, IN_CHANNEL), device=device)\n",
        "\n",
        "      # each prediction involves multiple generation steps\n",
        "      for i in range(5):\n",
        "        out, restored = model(restored[:, :MAX_LENGTH, :], x[\"image_clip\"].unsqueeze(1), torch.zeros_like(x[\"image_clip\"], device=device).unsqueeze(1), torch.ones((x[\"input_ids\"].shape[0], MAX_LENGTH), device=device), torch.tensor([1, 0], device=device).repeat(x[\"attention_mask\"].shape[0], 1))\n",
        "      \n",
        "      # append final strings to each answer bin\n",
        "      indexes = nn.functional.softmax(out, dim=-1).argmax(dim=-1)\n",
        "      indexes = indexes.unique_consecutive(dim=-1)\n",
        "\n",
        "      ans_strs = [dataset.tokenizer.decode(index) for index in indexes]\n",
        "\n",
        "      GT_list = []\n",
        "      for image_name in x[\"image\"]:\n",
        "        GT_list.append(['[CLS] ' + caption.strip().lower() + ' [SEP]' for caption in dataset.data.loc[dataset.data['image'] == image_name][\"caption\"]])\n",
        "\n",
        "      acc_bleu += metric(ans_strs, GT_list)\n",
        "\n",
        "summary.write(f\"BLEU-4 score: {acc_bleu / len(val_loader)}\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "torch.save(val_set, f\"{MODEL_NAME}.valset\")\n",
        "\n",
        "if not summary == sys.stdout:\n",
        "  summary.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "!cp -av {MODEL_NAME} f'/gdrive/MyDrive/{MODEL_NAME}.pickle'"
      ]
    }
  ],
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    "accelerator": "GPU",
    "colab": {
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      "machine_shape": "hm",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3.9.12 ('mlenv')",
      "language": "python",
      "name": "python3"
    },
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        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "orig_nbformat": 4,
    "vscode": {
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