1-simple-training-demo.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_data_dir = \"/media/curttigges/project-files/datasets/ms-coco/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytorch_lightning as pl\n",
    "\n",
    "from pytorch_lightning.loggers import WandbLogger # Comment out if not using wandb\n",
    "from pytorch_lightning.callbacks import TQDMProgressBar\n",
    "from pytorch_lightning.callbacks import StochasticWeightAveraging\n",
    "\n",
    "from q2l_labeller.data.coco_data_module import COCODataModule\n",
    "from q2l_labeller.pl_modules.query2label_train_module import Query2LabelTrainModule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 42\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl.seed_everything(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_dict = {\n",
    "    \"backbone_desc\":\"resnest101e\",\n",
    "    \"conv_out_dim\":2048,\n",
    "    \"hidden_dim\":256,\n",
    "    \"num_encoders\":1,\n",
    "    \"num_decoders\":2,\n",
    "    \"num_heads\":8,\n",
    "    \"batch_size\":24,\n",
    "    \"image_dim\":576,\n",
    "    \"learning_rate\":0.0001, \n",
    "    \"momentum\":0.9,\n",
    "    \"weight_decay\":0.01, \n",
    "    \"n_classes\":80,\n",
    "    \"thresh\":0.5,\n",
    "    \"use_cutmix\":True,\n",
    "    \"use_pos_encoding\":False,\n",
    "    \"loss\":\"ASL\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "coco = COCODataModule(\n",
    "    img_data_dir,\n",
    "    img_size=param_dict[\"image_dim\"],\n",
    "    batch_size=param_dict[\"batch_size\"],\n",
    "    num_workers=24,\n",
    "    use_cutmix=param_dict[\"use_cutmix\"],\n",
    "    cutmix_alpha=1.0)\n",
    "param_dict[\"data\"] = coco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pl_model = Query2LabelTrainModule(**param_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mcurt-tigges\u001b[0m (\u001b[33mascendant\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.13.1 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.12.21"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>training/logs/wandb/run-20220811_171953-3h1bndrz</code>"
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     "data": {
      "text/html": [
       "Syncing run <strong><a href=\"https://wandb.ai/ascendant/coco-labeling/runs/3h1bndrz\" target=\"_blank\">charmed-fog-115</a></strong> to <a href=\"https://wandb.ai/ascendant/coco-labeling\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
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       "<IPython.core.display.HTML object>"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: logging graph, to disable use `wandb.watch(log_graph=False)`\n",
      "Using 16bit native Automatic Mixed Precision (AMP)\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=7.63s)\n",
      "creating index...\n",
      "index created!\n",
      "loading annotations into memory...\n",
      "Done (t=4.61s)\n",
      "creating index...\n",
      "index created!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name           | Type           | Params\n",
      "--------------------------------------------------\n",
      "0 | model          | Query2Label    | 52.9 M\n",
      "1 | base_criterion | AsymmetricLoss | 0     \n",
      "--------------------------------------------------\n",
      "52.9 M    Trainable params\n",
      "0         Non-trainable params\n",
      "52.9 M    Total params\n",
      "105.766   Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23: 100%|██████████| 5138/5138 [20:54:23<00:00, 14.65s/it, loss=0.0583, v_num=ndrz, val_loss=nan.0, val_rmap=0.895, val_cat_prec=0.736, val_cat_recall=0.735, val_cat_f1=0.722, val_ovr_prec=0.832, val_ovr_recall=0.829, val_ovr_f1=0.829]      \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "wandb: Network error (ReadTimeout), entering retry loop.\n"
     ]
    }
   ],
   "source": [
    "# Comment out if not using wandb\n",
    "wandb_logger = WandbLogger(\n",
    "    project=\"coco-labeling\", \n",
    "    save_dir=\"training/logs/\",\n",
    "    log_model=True)\n",
    "wandb_logger.watch(pl_model, log=\"all\")\n",
    "\n",
    "trainer = pl.Trainer(\n",
    "    max_epochs=24,\n",
    "    precision=16,\n",
    "    accelerator='gpu', \n",
    "    devices=1,\n",
    "    logger=wandb_logger, # Comment out if not using wandb\n",
    "    default_root_dir=\"training/checkpoints/\",\n",
    "    callbacks=[TQDMProgressBar(refresh_rate=10)])\n",
    "trainer.fit(pl_model, param_dict[\"data\"])"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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