train.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "HvW3xrAPCJs6",
        "outputId": "9dfddc6d-b749-4e29-ab20-cab4e594e0cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'structure_generation'...\n",
            "remote: Enumerating objects: 14, done.\u001b[K\n",
            "remote: Counting objects: 100% (14/14), done.\u001b[K\n",
            "remote: Compressing objects: 100% (11/11), done.\u001b[K\n",
            "remote: Total 14 (delta 0), reused 7 (delta 0), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (14/14), 910.60 KiB | 12.30 MiB/s, done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/evgmaslov/structure_generation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "import sys\n",
        "repo = Path(\"/content/structure_generation\").resolve()\n",
        "sys.path.append(\"/content/structure_generation/\")"
      ],
      "metadata": {
        "id": "L6JC5uypohb2"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "AVmmf_OO3sod",
        "outputId": "3f3dc233-f548-4b07-ce0a-49dc24d9cdf0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading diffusers-0.27.2-py3-none-any.whl (2.0 MB)\n",
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            "\u001b[?25hCollecting accelerate (from -r requirements.txt (line 2))\n",
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            "  Downloading datasets-2.19.0-py3-none-any.whl (542 kB)\n",
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            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.10.0->accelerate->-r requirements.txt (line 2))\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
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            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.10.0->accelerate->-r requirements.txt (line 2))\n",
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            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.10.0->accelerate->-r requirements.txt (line 2))\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
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            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.10.0->accelerate->-r requirements.txt (line 2))\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
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            "\u001b[?25hCollecting multiprocess (from datasets->diffusers[training]->-r requirements.txt (line 1))\n",
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            "Installing collected packages: xxhash, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, einops, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, huggingface-hub, nvidia-cusolver-cu12, diffusers, datasets, accelerate, peft\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.20.3\n",
            "    Uninstalling huggingface-hub-0.20.3:\n",
            "      Successfully uninstalled huggingface-hub-0.20.3\n",
            "Successfully installed accelerate-0.29.3 datasets-2.19.0 diffusers-0.27.2 dill-0.3.8 einops-0.7.0 huggingface-hub-0.22.2 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 peft-0.10.0 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!cd {repo} && pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "A_1gniqjBPNf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87,
          "referenced_widgets": [
            "251c7c170e0c412d92c4f871279932a2",
            "5e01159471c74f5496c61113c3480523",
            "d883d6dd1e16427da023372159511852",
            "9a008adfb32c4082831378cd7a0a0508",
            "98e6da5ad29b405986e079d398d72d44",
            "2561b42308e84c34b1b96350eb97b75d",
            "2a832814bfa64a019ebd377229a2cca3",
            "79f5732123034dcd9e2107d6cde49960",
            "1325a468b1f7479ca0d411facb20ad17",
            "20f53f1651b44bebbaa43a6c1f122c81",
            "edc19e0f69ae4ba98505afab589a0374"
          ]
        },
        "outputId": "fcbe188f-b2b1-420e-8d27-224fb46ef60a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "251c7c170e0c412d92c4f871279932a2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "from torch import nn\n",
        "import math\n",
        "from typing import Optional, Tuple, Union, List\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tqdm.notebook import tqdm\n",
        "import shutil\n",
        "from diffusers import DDPMScheduler, UNet3DConditionModel, UNet2DModel, DDPMPipeline\n",
        "from diffusers.optimization import get_cosine_schedule_with_warmup\n",
        "from accelerate import Accelerator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7h3nGuprnX8a"
      },
      "source": [
        "## Downloading sources, if you use kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gUxRgLceE2Wm"
      },
      "outputs": [],
      "source": [
        "!conda install -y gdown"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0_eFLMB6E3CU"
      },
      "outputs": [],
      "source": [
        "import gdown\n",
        "url = 'https://drive.google.com/uc?export=download&id=1hDhDttFpZ_-TDVo_-yBzhafh75mvis6L'\n",
        "data_path = '/kaggle/working/dataset.zip'\n",
        "gdown.download(url, data_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlnrEK3bnX8c"
      },
      "source": [
        "## Downloading sources, if you use colab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gB8oYAF6OGiF",
        "outputId": "ca62c567-db7f-48ce-bb25-4d402564d19b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "data_path = \"/content/drive/MyDrive/structure_generation/dataset.zip\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "81vUu9BmnX8d"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "5Mz_0dvsE9hR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "outputId": "851a6f31-10ee-4503-edc0-175e005b93d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mВыходные данные были обрезаны до нескольких последних строк (5000).\u001b[0m\n",
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          ]
        }
      ],
      "source": [
        "!unzip {data_path}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "YqesUkUhnX8e"
      },
      "outputs": [],
      "source": [
        "from data import TesselationDataset, collate_fn\n",
        "from models import DiffusersDDPM3D\n",
        "from utils import Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "lACDik6qZMsj"
      },
      "outputs": [],
      "source": [
        "train_names, val_names = train_test_split(os.listdir(\"/content/dataset\")[:1000], test_size=0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "lsyJfdO84cBL"
      },
      "outputs": [],
      "source": [
        "batch_size = 2\n",
        "train_data_params = {\n",
        "    \"tess_dir\":\"/content/dataset\",\n",
        "    \"names\":train_names,\n",
        "    \"dims\":3\n",
        "}\n",
        "train_dataset = TesselationDataset(train_data_params)\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=lambda x: collate_fn(x, train_dataset))\n",
        "\n",
        "val_data_params = {\n",
        "    \"tess_dir\":\"/content/dataset\",\n",
        "    \"names\":val_names,\n",
        "    \"dims\":3\n",
        "}\n",
        "val_dataset = TesselationDataset(val_data_params)\n",
        "val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, collate_fn=lambda x: collate_fn(x, val_dataset))\n",
        "\n",
        "trainer_args = {\n",
        "    \"train_dataloader\":train_dataloader,\n",
        "    \"val_dataloader\":val_dataloader,\n",
        "    \"score\":None,\n",
        "    \"train_score_computing_frequency\":1,\n",
        "    \"optimizer\":torch.optim.AdamW,\n",
        "    \"optimizer_args\":{\"lr\":3e-4},\n",
        "    \"scheduler\":get_cosine_schedule_with_warmup,\n",
        "    \"scheduler_args\":{\n",
        "        \"num_warmup_steps\":100,\n",
        "        \"num_training_steps\":(len(train_dataloader) * 2)\n",
        "    },\n",
        "    \"freezer\":None,\n",
        "    \"freezer_args\":None,\n",
        "    \"training_controller\":None,\n",
        "    \"n_epochs\":50,\n",
        "    \"device\":\"cuda\" if torch.cuda.is_available() else \"cpu\",\n",
        "    \"backup_path\":\"/content/drive/MyDrive/structure_generation/model_difusers3d_v3.pt\",\n",
        "    \"show_outputs\":True,\n",
        "    \"show_outputs_every\":2,\n",
        "    \"n_outputs\":1,\n",
        "    \"is_saving_history\":False,\n",
        "}\n",
        "\n",
        "trainer = Trainer(trainer_args)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "6FuuGVv85aAq"
      },
      "outputs": [],
      "source": [
        "model_args = {\n",
        "    \"device\":trainer.device,\n",
        "    \"sample_shape\": (32, 32, 32),\n",
        "    \"noise_scheduler\": DDPMScheduler(num_train_timesteps=1000),\n",
        "    \"unet_n_blocks\": 2,\n",
        "    \"unet_channels\": 64,\n",
        "    \"is_attn\":(False, False, True, True),\n",
        "    \"ch_mults\":(1, 2, 2, 4),\n",
        "    \"channels\":(64, 128, 128, 256),\n",
        "}\n",
        "model = DiffusersDDPM3D(model_args)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "2R_qeOPXK5dr"
      },
      "outputs": [],
      "source": [
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "j6XzDqOxJy0A"
      },
      "outputs": [],
      "source": [
        "trainer.train(model)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O_6dJrYdp1tg"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
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