DeMansia train.ipynb

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   "source": [
    "from dataclasses import asdict\n",
    "\n",
    "import pytorch_lightning as pl\n",
    "from lightning.pytorch import loggers as pl_loggers\n",
    "from pytorch_lightning.callbacks import LearningRateMonitor\n",
    "from timm.data import create_dataset, create_loader\n",
    "\n",
    "from model import DeMansia\n",
    "from model_config import DeMansia_tiny_config\n",
    "from modules.data import create_token_label_dataset, create_token_label_loader\n",
    "from modules.ema import EMA, EMAModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "imagenet_root = \"datasets/ImageNet 1k\"\n",
    "token_label_root = \"datasets/ImageNet 1k token label\""
   ]
  },
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   "source": [
    "config = asdict(DeMansia_tiny_config())\n",
    "model = DeMansia(**config)"
   ]
  },
  {
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   "source": [
    "class dataset(pl.LightningDataModule):\n",
    "    def __init__(self, batch_size: int):\n",
    "        super().__init__()\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "    def setup(self, stage: str):\n",
    "        self.train_set = create_token_label_dataset(\n",
    "            imagenet_root + \"/train\", token_label_root\n",
    "        )\n",
    "        self.valid_set = create_dataset(\n",
    "            name=\"\", root=imagenet_root + \"/val\", batch_size=self.batch_size\n",
    "        )\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return create_token_label_loader(\n",
    "            self.train_set,\n",
    "            input_size=config[\"img_size\"],\n",
    "            batch_size=self.batch_size,\n",
    "            num_workers=16,\n",
    "            use_prefetcher=False, # some weird bug with prefetcher, as long as you have a normal ssd the loading speed won't be a issue\n",
    "            pin_memory=True,\n",
    "        )\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        return create_loader(\n",
    "            self.valid_set,\n",
    "            input_size=config[\"img_size\"],\n",
    "            batch_size=self.batch_size,\n",
    "            is_training=False,\n",
    "            num_workers=16,\n",
    "            use_prefetcher=False,\n",
    "            pin_memory=True,\n",
    "        )\n",
    "\n",
    "\n",
    "data = dataset(batch_size=768)"
   ]
  },
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   "source": [
    "trainer = pl.Trainer(\n",
    "    callbacks=[\n",
    "        EMA(decay=0.9999),\n",
    "        EMAModelCheckpoint(\n",
    "            dirpath=\"models/\",\n",
    "            save_top_k=-1,\n",
    "        ),\n",
    "        LearningRateMonitor(logging_interval=\"step\"),\n",
    "    ],\n",
    "    logger=pl_loggers.WandbLogger(project=\"DeMansia Tiny\", name=\"Pretrain\"),\n",
    "    precision=\"bf16-mixed\",\n",
    "    max_epochs=310,\n",
    ")\n",
    "\n",
    "trainer.fit(model, data)\n",
    "# trainer.fit(model, data, ckpt_path=\"ckpt to resume training\")"
   ]
  }
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