notebooks/train_playground.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Columnformer training playground"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation\n",
    "\n",
    "If working on colab, you need to install the project. Skip these steps if working from a local installation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "76tI5bzpxIE5"
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "INSTALL=false\n",
    "\n",
    "if [[ $INSTALL == true ]]; then\n",
    "    git clone https://github.com/clane9/columnformers.git\n",
    "    cd columnformers\n",
    "\n",
    "    pip install -U pip\n",
    "    pip install -r requirements.txt\n",
    "    pip install -e .\n",
    "fi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import time\n",
    "from dataclasses import dataclass\n",
    "from typing import Literal, Optional, Tuple, Union\n",
    "\n",
    "import torch\n",
    "import yaml\n",
    "from fvcore.nn import FlopCountAnalysis\n",
    "from timm.utils import AverageMeter, random_seed\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "import columnformers.utils as ut\n",
    "from columnformers.data import create_dataset, create_loader, list_datasets\n",
    "from columnformers.inspection.metrics import Accuracy\n",
    "from columnformers.models import create_model, list_models\n",
    "from columnformers.tasks import ImageClassification, Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Args:\n",
    "    # Model\n",
    "    model: str = \"vision_columnformer_r_tiny_patch16_128\"\n",
    "    num_heads: Optional[int] = None\n",
    "    mlp_ratio: Optional[int] = None\n",
    "    untied: Optional[Union[bool, Tuple[bool, bool, bool]]] = None\n",
    "    skip_attn: Optional[bool] = None\n",
    "    attn_bias: Optional[bool] = None\n",
    "    qk_head_dim: Optional[int] = None\n",
    "    no_vp: Optional[bool] = None\n",
    "    init_local_attn: Optional[bool] = None\n",
    "    global_pool: Literal[\"avg\", \"spatial\"] = \"avg\"\n",
    "    pos_embed: bool = True\n",
    "    drop_rate: float = 0.0\n",
    "    proj_drop_rate: float = 0.0\n",
    "    attn_drop_rate: float = 0.0\n",
    "    wiring_lambd: float = 0.0\n",
    "    # Dataset\n",
    "    dataset: str = \"imagenet100\"\n",
    "    crop_min_scale: float = 1.0\n",
    "    hflip: float = 0.01\n",
    "    color_jitter: Optional[float] = None\n",
    "    keep_in_memory: bool = False\n",
    "    workers: int = 4\n",
    "    # Optimization\n",
    "    epochs: int = 100\n",
    "    batch_size: int = 256\n",
    "    lr: float = 6e-4\n",
    "    decay_lr: bool = True\n",
    "    warmup_fraction: float = 0.1\n",
    "    weight_decay: float = 0.05\n",
    "    clip_grad: Optional[float] = 1.0\n",
    "    # Logistics\n",
    "    use_cuda: bool = True\n",
    "    log_interval: int = 10\n",
    "    debug: bool = False\n",
    "    seed: int = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available models: \n",
      "vision_transformer_tiny_patch16_128\n",
      "vision_columnformer_ff_tiny_patch16_128\n",
      "vision_columnformer_r_tiny_patch16_128 \n",
      "\n",
      "Available datasets: \n",
      "imagenet-100\n",
      "micro-imagenet-100\n",
      "debug-100\n"
     ]
    }
   ],
   "source": [
    "print(\"Available models:\", \"\\n\" + \"\\n\".join(list_models()), \"\\n\")\n",
    "print(\"Available datasets:\", \"\\n\" + \"\\n\".join(list_datasets()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model: vision_transformer_tiny_patch16_128\n",
      "num_heads: null\n",
      "mlp_ratio: null\n",
      "untied: null\n",
      "skip_attn: null\n",
      "attn_bias: null\n",
      "qk_head_dim: null\n",
      "no_vp: null\n",
      "init_local_attn: null\n",
      "global_pool: avg\n",
      "pos_embed: true\n",
      "drop_rate: 0.0\n",
      "proj_drop_rate: 0.0\n",
      "attn_drop_rate: 0.0\n",
      "wiring_lambd: 0.0\n",
      "dataset: micro-imagenet-100\n",
      "crop_min_scale: 1.0\n",
      "hflip: 0.01\n",
      "color_jitter: null\n",
      "keep_in_memory: true\n",
      "workers: 4\n",
      "epochs: 3\n",
      "batch_size: 256\n",
      "lr: 0.001\n",
      "decay_lr: false\n",
      "warmup_fraction: 0.0\n",
      "weight_decay: 0.05\n",
      "clip_grad: 1.0\n",
      "use_cuda: true\n",
      "log_interval: 10\n",
      "debug: false\n",
      "seed: 42\n",
      "\n"
     ]
    }
   ],
   "source": [
    "args = Args(\n",
    "    model=\"vision_transformer_tiny_patch16_128\",\n",
    "    dataset=\"micro-imagenet-100\",\n",
    "    keep_in_memory=True,\n",
    "    epochs=3,\n",
    "    lr=1e-3,\n",
    "    decay_lr=False,\n",
    "    warmup_fraction=0.0,\n",
    ")\n",
    "\n",
    "print(yaml.safe_dump(args.__dict__, sort_keys=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on: cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if args.use_cuda and torch.cuda.is_available() else \"cpu\")\n",
    "print(\"Running on:\", device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_seed(args.seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fbb1bb9d47cb47dc8b3051853a5311e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44f4606692a54843b6f853ae05e381d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Resolving data files:   0%|          | 0/17 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['image', 'label'],\n",
      "        num_rows: 120000\n",
      "    })\n",
      "    validation: Dataset({\n",
      "        features: ['image', 'label'],\n",
      "        num_rows: 5000\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "dataset = create_dataset(\n",
    "    args.dataset,\n",
    "    min_scale=args.crop_min_scale,\n",
    "    hflip=args.hflip,\n",
    "    color_jitter=args.color_jitter,\n",
    "    keep_in_memory=args.keep_in_memory,\n",
    ")\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaders = {}\n",
    "for split, ds in dataset.items():\n",
    "    loaders[split] = create_loader(\n",
    "        ds,\n",
    "        shuffle=True,\n",
    "        batch_size=args.batch_size,\n",
    "        drop_last=False,\n",
    "        num_workers=args.workers,\n",
    "        device=device,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'image': (torch.Size([256, 3, 128, 128]), torch.float32, device(type='cuda', index=0)), 'label': (torch.Size([256]), torch.int64, device(type='cuda', index=0))}\n"
     ]
    }
   ],
   "source": [
    "first_batch = next(iter(loaders[\"train\"]))\n",
    "\n",
    "print({k: (v.shape, v.dtype, v.device) for k, v in first_batch.items()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = dataset[\"train\"].features[\"label\"].num_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VisionColumnformer(\n",
      "  pos_embed=True\n",
      "  (patch_embed): PatchEmbed(\n",
      "    (proj): Conv2d(3, 384, kernel_size=(16, 16), stride=(16, 16))\n",
      "    (norm): Identity()\n",
      "  )\n",
      "  (encoder): Columnformer(\n",
      "    depth=6, recurrent=False, geometry=(64, 64), init_local_attn=False, local_attn_sigma=2.0\n",
      "    (blocks): ModuleList(\n",
      "      (0-5): 6 x Block(\n",
      "        skip_attn=True\n",
      "        (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
      "        (attn): Attention(\n",
      "          bias=False\n",
      "          (q): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (k): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (v): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
      "          (act): GELU(approximate='none')\n",
      "          (drop1): Dropout(p=0.0, inplace=False)\n",
      "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
      "          (drop2): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
      "  (pool): None\n",
      "  (head_drop): Dropout(p=0.0, inplace=False)\n",
      "  (head): Linear(in_features=384, out_features=100, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = create_model(\n",
    "    args.model,\n",
    "    num_heads=args.num_heads,\n",
    "    mlp_ratio=args.mlp_ratio,\n",
    "    untied=args.untied,\n",
    "    skip_attn=args.skip_attn,\n",
    "    attn_bias=args.attn_bias,\n",
    "    qk_head_dim=args.qk_head_dim,\n",
    "    no_vp=args.no_vp,\n",
    "    init_local_attn=args.init_local_attn,\n",
    "    num_classes=num_classes,\n",
    "    pos_embed=args.pos_embed,\n",
    "    global_pool=args.global_pool,\n",
    "    drop_rate=args.drop_rate,\n",
    "    proj_drop_rate=args.proj_drop_rate,\n",
    "    attn_drop_rate=args.attn_drop_rate,\n",
    ")\n",
    "model = model.to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ImageClassification(wiring_lambd=0.0)\n"
     ]
    }
   ],
   "source": [
    "task = ImageClassification(wiring_lambd=args.wiring_lambd)\n",
    "task = task.to(device)\n",
    "print(task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Unsupported operator aten::add encountered 13 time(s)\n",
      "Unsupported operator aten::mul encountered 6 time(s)\n",
      "Unsupported operator aten::softmax encountered 6 time(s)\n",
      "Unsupported operator aten::gelu encountered 6 time(s)\n",
      "Unsupported operator aten::mean encountered 1 time(s)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params: 11M, FLOPs: 719M\n"
     ]
    }
   ],
   "source": [
    "param_count = sum(p.numel() for p in model.parameters())\n",
    "flop_count = flops = FlopCountAnalysis(\n",
    "    model, first_batch[\"image\"][:1]\n",
    ").total()\n",
    "\n",
    "print(f\"Params: {param_count / 1e6:.0f}M, FLOPs: {flop_count / 1e6:.0f}M\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdamW (\n",
      "Parameter Group 0\n",
      "    amsgrad: False\n",
      "    betas: (0.9, 0.95)\n",
      "    capturable: False\n",
      "    differentiable: False\n",
      "    eps: 1e-08\n",
      "    foreach: None\n",
      "    fused: None\n",
      "    lr: 0.001\n",
      "    maximize: False\n",
      "    weight_decay: 0.05\n",
      "\n",
      "Parameter Group 1\n",
      "    amsgrad: False\n",
      "    betas: (0.9, 0.95)\n",
      "    capturable: False\n",
      "    differentiable: False\n",
      "    eps: 1e-08\n",
      "    foreach: None\n",
      "    fused: None\n",
      "    lr: 0.001\n",
      "    maximize: False\n",
      "    weight_decay: 0.0\n",
      ")\n",
      "No decay keys: ['pos_embed', 'patch_embed.proj.bias', 'encoder.blocks.0.norm1.weight', 'encoder.blocks.0.norm1.bias', 'encoder.blocks.0.attn.q.bias', 'encoder.blocks.0.attn.k.bias', 'encoder.blocks.0.attn.v.bias', 'encoder.blocks.0.attn.proj.bias', 'encoder.blocks.0.norm2.weight', 'encoder.blocks.0.norm2.bias', 'encoder.blocks.0.mlp.fc1.bias', 'encoder.blocks.0.mlp.fc2.bias', 'encoder.blocks.1.norm1.weight', 'encoder.blocks.1.norm1.bias', 'encoder.blocks.1.attn.q.bias', 'encoder.blocks.1.attn.k.bias', 'encoder.blocks.1.attn.v.bias', 'encoder.blocks.1.attn.proj.bias', 'encoder.blocks.1.norm2.weight', 'encoder.blocks.1.norm2.bias', 'encoder.blocks.1.mlp.fc1.bias', 'encoder.blocks.1.mlp.fc2.bias', 'encoder.blocks.2.norm1.weight', 'encoder.blocks.2.norm1.bias', 'encoder.blocks.2.attn.q.bias', 'encoder.blocks.2.attn.k.bias', 'encoder.blocks.2.attn.v.bias', 'encoder.blocks.2.attn.proj.bias', 'encoder.blocks.2.norm2.weight', 'encoder.blocks.2.norm2.bias', 'encoder.blocks.2.mlp.fc1.bias', 'encoder.blocks.2.mlp.fc2.bias', 'encoder.blocks.3.norm1.weight', 'encoder.blocks.3.norm1.bias', 'encoder.blocks.3.attn.q.bias', 'encoder.blocks.3.attn.k.bias', 'encoder.blocks.3.attn.v.bias', 'encoder.blocks.3.attn.proj.bias', 'encoder.blocks.3.norm2.weight', 'encoder.blocks.3.norm2.bias', 'encoder.blocks.3.mlp.fc1.bias', 'encoder.blocks.3.mlp.fc2.bias', 'encoder.blocks.4.norm1.weight', 'encoder.blocks.4.norm1.bias', 'encoder.blocks.4.attn.q.bias', 'encoder.blocks.4.attn.k.bias', 'encoder.blocks.4.attn.v.bias', 'encoder.blocks.4.attn.proj.bias', 'encoder.blocks.4.norm2.weight', 'encoder.blocks.4.norm2.bias', 'encoder.blocks.4.mlp.fc1.bias', 'encoder.blocks.4.mlp.fc2.bias', 'encoder.blocks.5.norm1.weight', 'encoder.blocks.5.norm1.bias', 'encoder.blocks.5.attn.q.bias', 'encoder.blocks.5.attn.k.bias', 'encoder.blocks.5.attn.v.bias', 'encoder.blocks.5.attn.proj.bias', 'encoder.blocks.5.norm2.weight', 'encoder.blocks.5.norm2.bias', 'encoder.blocks.5.mlp.fc1.bias', 'encoder.blocks.5.mlp.fc2.bias', 'norm.bias', 'head.bias']\n"
     ]
    }
   ],
   "source": [
    "no_decay_keys = ut.get_no_decay_keys(model)\n",
    "optimizer = ut.create_optimizer(\n",
    "    model,\n",
    "    no_decay_keys=no_decay_keys,\n",
    "    lr=args.lr,\n",
    "    weight_decay=args.weight_decay,\n",
    ")\n",
    "epoch_steps = len(loaders[\"train\"])\n",
    "lr_schedule = ut.CosineDecaySchedule(\n",
    "    base_lr=args.lr,\n",
    "    total_steps=args.epochs * epoch_steps,\n",
    "    do_decay=args.decay_lr,\n",
    "    warmup_fraction=args.warmup_fraction,\n",
    ")\n",
    "print(optimizer)\n",
    "print(\"No decay keys:\", no_decay_keys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_one_epoch(\n",
    "    *,\n",
    "    args: Args,\n",
    "    epoch: int,\n",
    "    model: torch.nn.Module,\n",
    "    task: Task,\n",
    "    train_loader: DataLoader,\n",
    "    optimizer: torch.optim.Optimizer,\n",
    "    lr_schedule: ut.LRSchedule,\n",
    "    device: torch.device,\n",
    "):\n",
    "    model.train()\n",
    "    task.train()\n",
    "    optimizer.zero_grad()\n",
    "    \n",
    "    is_cuda = device.type == \"cuda\"\n",
    "    if is_cuda:\n",
    "        torch.cuda.empty_cache()\n",
    "        torch.cuda.reset_peak_memory_stats()\n",
    "    \n",
    "    accuracy = Accuracy()\n",
    "\n",
    "    loss_m = AverageMeter()\n",
    "    data_time_m = AverageMeter()\n",
    "    step_time_m = AverageMeter()\n",
    "    acc_m = AverageMeter()\n",
    "\n",
    "    epoch_batches = len(train_loader)\n",
    "    first_step = epoch * epoch_batches\n",
    "\n",
    "    end = time.monotonic()\n",
    "    for batch_idx, batch in enumerate(train_loader):\n",
    "        step = first_step + batch_idx\n",
    "        is_last_batch = batch_idx + 1 == epoch_batches\n",
    "        batch_size = len(batch[\"image\"])\n",
    "        data_time = time.monotonic() - end\n",
    "\n",
    "        # forward pass\n",
    "        loss, state = task.forward(model, batch)\n",
    "        loss_item = loss.item()\n",
    "\n",
    "        if math.isnan(loss_item) or math.isinf(loss_item):\n",
    "            raise RuntimeError(\"NaN/Inf loss encountered on step %d; exiting\", step)\n",
    "\n",
    "        # update lr\n",
    "        lr = lr_schedule(step)\n",
    "        ut.update_lr_(optimizer, lr)\n",
    "\n",
    "        # backward and optimization step\n",
    "        total_norm = ut.backward_step(loss, optimizer, max_grad_norm=args.clip_grad)\n",
    "\n",
    "        # end of iteration timing\n",
    "        if is_cuda:\n",
    "            torch.cuda.synchronize()\n",
    "        step_time = time.monotonic() - end\n",
    "        \n",
    "        loss_m.update(loss_item, batch_size)\n",
    "        data_time_m.update(data_time, batch_size)\n",
    "        step_time_m.update(step_time, batch_size)\n",
    "        \n",
    "        acc_item = accuracy(state)\n",
    "        acc_m.update(acc_item, batch_size)\n",
    "\n",
    "        if step % args.log_interval == 0 or is_last_batch or args.debug:\n",
    "            tput = args.batch_size / step_time_m.avg\n",
    "            if is_cuda:\n",
    "                alloc_mem_gb = torch.cuda.max_memory_allocated() / 1e9\n",
    "                res_mem_gb = torch.cuda.max_memory_reserved() / 1e9\n",
    "            else:\n",
    "                alloc_mem_gb = res_mem_gb = 0.0\n",
    "\n",
    "            print(\n",
    "                f\"Train: {epoch:>3d} [{batch_idx:>3d}/{epoch_batches}][{step:>6d}]\"\n",
    "                f\"  Loss: {loss_m.val:#.3g} ({loss_m.avg:#.3g})\"\n",
    "                f\"  Acc: {acc_m.val:#.3g} ({acc_m.avg:#.3g})\"\n",
    "                f\"  LR: {lr:.3e}\"\n",
    "                f\"  Grad: {total_norm:.3e}\"\n",
    "                f\"  Time: {data_time_m.avg:.3f},{step_time_m.avg:.3f} {tput:.0f}/s\"\n",
    "                f\"  Mem: {alloc_mem_gb:.2f},{res_mem_gb:.2f} GB\"\n",
    "            )\n",
    "\n",
    "        # Restart timer for next iteration\n",
    "        end = time.monotonic()\n",
    "\n",
    "        if args.debug:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def validate(\n",
    "    *,\n",
    "    args: Args,\n",
    "    epoch: int,\n",
    "    model: torch.nn.Module,\n",
    "    task: Task,\n",
    "    val_loader: DataLoader,\n",
    "    device: torch.device,\n",
    ") -> float:\n",
    "    model.eval()\n",
    "    task.eval()\n",
    "\n",
    "    is_cuda = device.type == \"cuda\"\n",
    "    if is_cuda:\n",
    "        torch.cuda.empty_cache()\n",
    "        torch.cuda.reset_peak_memory_stats()\n",
    "    \n",
    "    accuracy = Accuracy()\n",
    "\n",
    "    loss_m = AverageMeter()\n",
    "    data_time_m = AverageMeter()\n",
    "    step_time_m = AverageMeter()\n",
    "    acc_m = AverageMeter()\n",
    "\n",
    "    epoch_batches = len(val_loader)\n",
    "    end = time.monotonic()\n",
    "    for batch_idx, batch in enumerate(val_loader):\n",
    "        batch_size = len(batch[\"image\"])\n",
    "        data_time = time.monotonic() - end\n",
    "\n",
    "        loss, state = task.forward(model, batch)\n",
    "        loss_item = loss.item()\n",
    "\n",
    "        # end of iteration timing\n",
    "        if is_cuda:\n",
    "            torch.cuda.synchronize()\n",
    "        step_time = time.monotonic() - end\n",
    "\n",
    "        loss_m.update(loss_item, batch_size)\n",
    "        data_time_m.update(data_time, batch_size)\n",
    "        step_time_m.update(step_time, batch_size)\n",
    "        \n",
    "        acc_item = accuracy(state)\n",
    "        acc_m.update(acc_item, batch_size)\n",
    "\n",
    "        if (\n",
    "            batch_idx % args.log_interval == 0\n",
    "            or batch_idx + 1 == epoch_batches\n",
    "            or args.debug\n",
    "        ):\n",
    "            tput = args.batch_size / step_time_m.avg\n",
    "            if is_cuda: \n",
    "                alloc_mem_gb = torch.cuda.max_memory_allocated() / 1e9\n",
    "                res_mem_gb = torch.cuda.max_memory_reserved() / 1e9\n",
    "            else:\n",
    "                alloc_mem_gb = res_mem_gb = 0.0\n",
    "\n",
    "            print(\n",
    "                f\"Val: {epoch:>3d} [{batch_idx:>3d}/{epoch_batches}]\"\n",
    "                f\"  Loss: {loss_m.val:#.3g} ({loss_m.avg:#.3g})\"\n",
    "                f\"  Acc: {acc_m.val:#.3g} ({acc_m.avg:#.3g})\"\n",
    "                f\"  Time: {data_time_m.avg:.3f},{step_time_m.avg:.3f} {tput:.0f}/s\"\n",
    "                f\"  Mem: {alloc_mem_gb:.2f},{res_mem_gb:.2f} GB\"\n",
    "            )\n",
    "\n",
    "        if args.debug:\n",
    "            break\n",
    "\n",
    "        # Reset timer\n",
    "        end = time.monotonic()\n",
    "\n",
    "    return loss_m.avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting epoch 0\n",
      "Train:   0 [  0/469][     0]  Loss: 4.65 (4.65)  Acc: 0.00 (0.00)  LR: 1.000e-03  Grad: 1.440e+00  Time: 1.047,1.365 187/s  Mem: 3.26,3.43 GB\n",
      "Train:   0 [ 10/469][    10]  Loss: 4.48 (4.55)  Acc: 1.95 (2.38)  LR: 1.000e-03  Grad: 9.749e-01  Time: 0.115,0.248 1031/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [ 20/469][    20]  Loss: 4.32 (4.48)  Acc: 3.52 (3.09)  LR: 1.000e-03  Grad: 9.567e-01  Time: 0.071,0.196 1307/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [ 30/469][    30]  Loss: 4.43 (4.44)  Acc: 3.91 (3.19)  LR: 1.000e-03  Grad: 8.931e-01  Time: 0.057,0.178 1438/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [ 40/469][    40]  Loss: 4.30 (4.42)  Acc: 4.69 (3.54)  LR: 1.000e-03  Grad: 9.022e-01  Time: 0.050,0.170 1509/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [ 50/469][    50]  Loss: 4.32 (4.40)  Acc: 5.86 (3.84)  LR: 1.000e-03  Grad: 9.025e-01  Time: 0.045,0.164 1560/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [ 60/469][    60]  Loss: 4.32 (4.38)  Acc: 4.30 (3.97)  LR: 1.000e-03  Grad: 9.220e-01  Time: 0.042,0.161 1593/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [ 70/469][    70]  Loss: 4.11 (4.35)  Acc: 8.59 (4.41)  LR: 1.000e-03  Grad: 9.137e-01  Time: 0.040,0.158 1622/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [ 80/469][    80]  Loss: 4.19 (4.33)  Acc: 7.03 (4.76)  LR: 1.000e-03  Grad: 9.014e-01  Time: 0.038,0.156 1643/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [ 90/469][    90]  Loss: 4.11 (4.31)  Acc: 5.47 (5.08)  LR: 1.000e-03  Grad: 8.789e-01  Time: 0.037,0.154 1661/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [100/469][   100]  Loss: 4.10 (4.29)  Acc: 7.03 (5.32)  LR: 1.000e-03  Grad: 9.445e-01  Time: 0.036,0.153 1675/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [110/469][   110]  Loss: 4.09 (4.26)  Acc: 7.42 (5.68)  LR: 1.000e-03  Grad: 9.544e-01  Time: 0.035,0.152 1686/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [120/469][   120]  Loss: 3.96 (4.24)  Acc: 7.03 (5.97)  LR: 1.000e-03  Grad: 1.011e+00  Time: 0.034,0.151 1695/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [130/469][   130]  Loss: 3.96 (4.22)  Acc: 10.2 (6.33)  LR: 1.000e-03  Grad: 1.125e+00  Time: 0.033,0.150 1705/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [140/469][   140]  Loss: 3.96 (4.20)  Acc: 9.38 (6.57)  LR: 1.000e-03  Grad: 1.022e+00  Time: 0.033,0.150 1711/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [150/469][   150]  Loss: 3.92 (4.18)  Acc: 7.81 (6.90)  LR: 1.000e-03  Grad: 1.102e+00  Time: 0.032,0.149 1718/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [160/469][   160]  Loss: 3.92 (4.16)  Acc: 11.7 (7.15)  LR: 1.000e-03  Grad: 1.175e+00  Time: 0.032,0.149 1723/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [170/469][   170]  Loss: 3.78 (4.14)  Acc: 12.1 (7.47)  LR: 1.000e-03  Grad: 1.145e+00  Time: 0.032,0.148 1729/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [180/469][   180]  Loss: 3.69 (4.12)  Acc: 15.2 (7.80)  LR: 1.000e-03  Grad: 1.085e+00  Time: 0.031,0.148 1733/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [190/469][   190]  Loss: 3.72 (4.10)  Acc: 12.9 (8.14)  LR: 1.000e-03  Grad: 1.144e+00  Time: 0.031,0.147 1738/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [200/469][   200]  Loss: 3.63 (4.08)  Acc: 16.8 (8.46)  LR: 1.000e-03  Grad: 1.046e+00  Time: 0.031,0.147 1740/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [210/469][   210]  Loss: 3.54 (4.06)  Acc: 21.5 (8.83)  LR: 1.000e-03  Grad: 1.121e+00  Time: 0.031,0.147 1744/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [220/469][   220]  Loss: 3.59 (4.04)  Acc: 18.4 (9.13)  LR: 1.000e-03  Grad: 1.082e+00  Time: 0.030,0.147 1747/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [230/469][   230]  Loss: 3.51 (4.02)  Acc: 17.6 (9.43)  LR: 1.000e-03  Grad: 1.274e+00  Time: 0.030,0.146 1750/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [240/469][   240]  Loss: 3.59 (4.00)  Acc: 17.2 (9.73)  LR: 1.000e-03  Grad: 1.167e+00  Time: 0.030,0.146 1753/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [250/469][   250]  Loss: 3.44 (3.98)  Acc: 18.8 (10.1)  LR: 1.000e-03  Grad: 1.183e+00  Time: 0.030,0.146 1755/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [260/469][   260]  Loss: 3.43 (3.96)  Acc: 15.6 (10.3)  LR: 1.000e-03  Grad: 1.173e+00  Time: 0.030,0.146 1757/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [270/469][   270]  Loss: 3.49 (3.94)  Acc: 17.6 (10.7)  LR: 1.000e-03  Grad: 1.221e+00  Time: 0.029,0.145 1760/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [280/469][   280]  Loss: 3.41 (3.92)  Acc: 17.2 (10.9)  LR: 1.000e-03  Grad: 1.259e+00  Time: 0.029,0.145 1762/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [290/469][   290]  Loss: 3.41 (3.91)  Acc: 19.1 (11.2)  LR: 1.000e-03  Grad: 1.220e+00  Time: 0.029,0.145 1764/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [300/469][   300]  Loss: 3.50 (3.89)  Acc: 16.4 (11.5)  LR: 1.000e-03  Grad: 1.240e+00  Time: 0.029,0.145 1766/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [310/469][   310]  Loss: 3.14 (3.87)  Acc: 24.2 (11.8)  LR: 1.000e-03  Grad: 1.301e+00  Time: 0.029,0.145 1768/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [320/469][   320]  Loss: 3.28 (3.85)  Acc: 19.9 (12.1)  LR: 1.000e-03  Grad: 1.365e+00  Time: 0.029,0.145 1769/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [330/469][   330]  Loss: 3.21 (3.83)  Acc: 21.9 (12.4)  LR: 1.000e-03  Grad: 1.393e+00  Time: 0.029,0.145 1770/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [340/469][   340]  Loss: 3.18 (3.81)  Acc: 21.5 (12.8)  LR: 1.000e-03  Grad: 1.445e+00  Time: 0.029,0.145 1771/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [350/469][   350]  Loss: 3.35 (3.79)  Acc: 16.8 (13.1)  LR: 1.000e-03  Grad: 1.397e+00  Time: 0.028,0.144 1773/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [360/469][   360]  Loss: 3.14 (3.77)  Acc: 25.8 (13.4)  LR: 1.000e-03  Grad: 1.622e+00  Time: 0.028,0.144 1774/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [370/469][   370]  Loss: 3.01 (3.75)  Acc: 23.8 (13.7)  LR: 1.000e-03  Grad: 1.721e+00  Time: 0.028,0.144 1775/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [380/469][   380]  Loss: 3.03 (3.73)  Acc: 27.3 (14.0)  LR: 1.000e-03  Grad: 1.577e+00  Time: 0.028,0.144 1776/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [390/469][   390]  Loss: 2.92 (3.72)  Acc: 20.3 (14.3)  LR: 1.000e-03  Grad: 1.453e+00  Time: 0.028,0.144 1778/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [400/469][   400]  Loss: 2.78 (3.70)  Acc: 35.5 (14.6)  LR: 1.000e-03  Grad: 1.315e+00  Time: 0.028,0.144 1778/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [410/469][   410]  Loss: 2.91 (3.68)  Acc: 25.8 (14.9)  LR: 1.000e-03  Grad: 1.611e+00  Time: 0.028,0.144 1779/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [420/469][   420]  Loss: 2.80 (3.66)  Acc: 28.1 (15.3)  LR: 1.000e-03  Grad: 1.488e+00  Time: 0.028,0.144 1780/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [430/469][   430]  Loss: 2.66 (3.64)  Acc: 34.4 (15.6)  LR: 1.000e-03  Grad: 1.558e+00  Time: 0.028,0.144 1781/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [440/469][   440]  Loss: 2.64 (3.62)  Acc: 36.3 (16.0)  LR: 1.000e-03  Grad: 1.558e+00  Time: 0.028,0.144 1782/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [450/469][   450]  Loss: 2.72 (3.60)  Acc: 29.3 (16.4)  LR: 1.000e-03  Grad: 1.921e+00  Time: 0.028,0.144 1783/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [460/469][   460]  Loss: 2.62 (3.58)  Acc: 35.9 (16.8)  LR: 1.000e-03  Grad: 1.559e+00  Time: 0.028,0.144 1783/s  Mem: 3.60,3.78 GB\n",
      "Train:   0 [468/469][   468]  Loss: 2.43 (3.56)  Acc: 40.1 (17.1)  LR: 1.000e-03  Grad: 1.708e+00  Time: 0.028,0.143 1785/s  Mem: 3.60,3.78 GB\n",
      "Val:   0 [  0/20]  Loss: 4.58 (4.58)  Acc: 9.38 (9.38)  Time: 0.925,0.966 265/s  Mem: 0.88,1.36 GB\n",
      "Val:   0 [ 10/20]  Loss: 4.35 (4.29)  Acc: 12.1 (10.8)  Time: 0.164,0.206 1244/s  Mem: 1.23,1.71 GB\n",
      "Val:   0 [ 19/20]  Loss: 4.11 (4.30)  Acc: 8.82 (10.7)  Time: 0.136,0.176 1453/s  Mem: 1.23,1.71 GB\n",
      "Starting epoch 1\n",
      "Train:   1 [  1/469][   470]  Loss: 2.36 (2.44)  Acc: 42.2 (38.5)  LR: 1.000e-03  Grad: 1.624e+00  Time: 0.524,0.641 399/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [ 11/469][   480]  Loss: 2.36 (2.45)  Acc: 39.1 (38.3)  LR: 1.000e-03  Grad: 1.680e+00  Time: 0.121,0.237 1079/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [ 21/469][   490]  Loss: 2.25 (2.43)  Acc: 42.2 (38.2)  LR: 1.000e-03  Grad: 1.765e+00  Time: 0.080,0.197 1299/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [ 31/469][   500]  Loss: 2.31 (2.41)  Acc: 41.0 (38.5)  LR: 1.000e-03  Grad: 1.702e+00  Time: 0.064,0.181 1414/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [ 41/469][   510]  Loss: 2.27 (2.38)  Acc: 42.6 (39.1)  LR: 1.000e-03  Grad: 1.787e+00  Time: 0.055,0.172 1492/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [ 51/469][   520]  Loss: 2.31 (2.36)  Acc: 38.3 (39.6)  LR: 1.000e-03  Grad: 1.725e+00  Time: 0.050,0.166 1541/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [ 61/469][   530]  Loss: 2.22 (2.33)  Acc: 43.8 (40.6)  LR: 1.000e-03  Grad: 2.132e+00  Time: 0.046,0.162 1577/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [ 71/469][   540]  Loss: 2.17 (2.30)  Acc: 40.6 (41.2)  LR: 1.000e-03  Grad: 1.866e+00  Time: 0.043,0.159 1606/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [ 81/469][   550]  Loss: 2.09 (2.27)  Acc: 44.5 (41.9)  LR: 1.000e-03  Grad: 1.871e+00  Time: 0.041,0.157 1628/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [ 91/469][   560]  Loss: 2.03 (2.25)  Acc: 48.0 (42.5)  LR: 1.000e-03  Grad: 1.759e+00  Time: 0.039,0.156 1646/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [101/469][   570]  Loss: 2.08 (2.21)  Acc: 48.8 (43.4)  LR: 1.000e-03  Grad: 2.510e+00  Time: 0.038,0.154 1661/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [111/469][   580]  Loss: 1.74 (2.18)  Acc: 59.0 (44.4)  LR: 1.000e-03  Grad: 1.834e+00  Time: 0.037,0.153 1672/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [121/469][   590]  Loss: 1.87 (2.15)  Acc: 52.3 (45.1)  LR: 1.000e-03  Grad: 2.175e+00  Time: 0.036,0.152 1683/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [131/469][   600]  Loss: 1.67 (2.12)  Acc: 54.7 (46.0)  LR: 1.000e-03  Grad: 2.407e+00  Time: 0.036,0.152 1689/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [141/469][   610]  Loss: 1.71 (2.08)  Acc: 57.8 (46.8)  LR: 1.000e-03  Grad: 2.221e+00  Time: 0.035,0.151 1697/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [151/469][   620]  Loss: 1.53 (2.05)  Acc: 61.3 (47.6)  LR: 1.000e-03  Grad: 1.973e+00  Time: 0.034,0.150 1704/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [161/469][   630]  Loss: 1.48 (2.02)  Acc: 64.1 (48.6)  LR: 1.000e-03  Grad: 2.092e+00  Time: 0.034,0.150 1712/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [171/469][   640]  Loss: 1.35 (1.98)  Acc: 68.4 (49.6)  LR: 1.000e-03  Grad: 2.407e+00  Time: 0.033,0.149 1716/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [181/469][   650]  Loss: 1.28 (1.94)  Acc: 64.1 (50.4)  LR: 1.000e-03  Grad: 2.213e+00  Time: 0.033,0.149 1722/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [191/469][   660]  Loss: 1.13 (1.91)  Acc: 71.1 (51.4)  LR: 1.000e-03  Grad: 1.869e+00  Time: 0.032,0.148 1726/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [201/469][   670]  Loss: 1.23 (1.87)  Acc: 71.5 (52.4)  LR: 1.000e-03  Grad: 2.066e+00  Time: 0.032,0.148 1730/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [211/469][   680]  Loss: 1.06 (1.84)  Acc: 76.2 (53.2)  LR: 1.000e-03  Grad: 2.085e+00  Time: 0.032,0.148 1733/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [221/469][   690]  Loss: 1.29 (1.81)  Acc: 67.2 (54.1)  LR: 1.000e-03  Grad: 2.818e+00  Time: 0.032,0.147 1737/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [231/469][   700]  Loss: 1.01 (1.77)  Acc: 71.9 (55.0)  LR: 1.000e-03  Grad: 2.248e+00  Time: 0.031,0.147 1738/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [241/469][   710]  Loss: 0.877 (1.74)  Acc: 79.3 (55.8)  LR: 1.000e-03  Grad: 2.024e+00  Time: 0.031,0.147 1741/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [251/469][   720]  Loss: 0.871 (1.71)  Acc: 78.1 (56.6)  LR: 1.000e-03  Grad: 2.283e+00  Time: 0.031,0.147 1743/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [261/469][   730]  Loss: 0.951 (1.68)  Acc: 76.2 (57.3)  LR: 1.000e-03  Grad: 2.404e+00  Time: 0.031,0.147 1746/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [271/469][   740]  Loss: 0.788 (1.65)  Acc: 80.1 (58.1)  LR: 1.000e-03  Grad: 1.991e+00  Time: 0.031,0.146 1748/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [281/469][   750]  Loss: 0.681 (1.62)  Acc: 81.6 (58.9)  LR: 1.000e-03  Grad: 1.877e+00  Time: 0.030,0.146 1750/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [291/469][   760]  Loss: 0.673 (1.59)  Acc: 83.2 (59.7)  LR: 1.000e-03  Grad: 1.876e+00  Time: 0.030,0.146 1752/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [301/469][   770]  Loss: 0.648 (1.56)  Acc: 83.2 (60.5)  LR: 1.000e-03  Grad: 1.804e+00  Time: 0.030,0.146 1754/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [311/469][   780]  Loss: 0.721 (1.53)  Acc: 80.1 (61.3)  LR: 1.000e-03  Grad: 2.073e+00  Time: 0.030,0.146 1756/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [321/469][   790]  Loss: 0.578 (1.50)  Acc: 87.5 (62.0)  LR: 1.000e-03  Grad: 2.083e+00  Time: 0.030,0.146 1758/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [331/469][   800]  Loss: 0.433 (1.47)  Acc: 89.8 (62.7)  LR: 1.000e-03  Grad: 1.627e+00  Time: 0.030,0.146 1759/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [341/469][   810]  Loss: 0.532 (1.44)  Acc: 85.9 (63.4)  LR: 1.000e-03  Grad: 1.729e+00  Time: 0.030,0.145 1761/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [351/469][   820]  Loss: 0.442 (1.42)  Acc: 87.9 (64.1)  LR: 1.000e-03  Grad: 1.783e+00  Time: 0.029,0.145 1762/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [361/469][   830]  Loss: 0.588 (1.39)  Acc: 84.8 (64.7)  LR: 1.000e-03  Grad: 1.944e+00  Time: 0.029,0.145 1764/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [371/469][   840]  Loss: 0.607 (1.37)  Acc: 81.2 (65.3)  LR: 1.000e-03  Grad: 2.260e+00  Time: 0.029,0.145 1765/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [381/469][   850]  Loss: 0.455 (1.35)  Acc: 89.5 (65.9)  LR: 1.000e-03  Grad: 1.946e+00  Time: 0.029,0.145 1767/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [391/469][   860]  Loss: 0.425 (1.33)  Acc: 90.2 (66.5)  LR: 1.000e-03  Grad: 1.999e+00  Time: 0.029,0.145 1768/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [401/469][   870]  Loss: 0.501 (1.30)  Acc: 87.9 (67.0)  LR: 1.000e-03  Grad: 2.163e+00  Time: 0.029,0.145 1769/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [411/469][   880]  Loss: 0.426 (1.28)  Acc: 89.8 (67.5)  LR: 1.000e-03  Grad: 1.672e+00  Time: 0.029,0.145 1770/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [421/469][   890]  Loss: 0.374 (1.26)  Acc: 93.0 (68.1)  LR: 1.000e-03  Grad: 1.785e+00  Time: 0.029,0.145 1771/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [431/469][   900]  Loss: 0.405 (1.24)  Acc: 90.6 (68.5)  LR: 1.000e-03  Grad: 1.795e+00  Time: 0.029,0.144 1772/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [441/469][   910]  Loss: 0.280 (1.22)  Acc: 93.4 (69.0)  LR: 1.000e-03  Grad: 1.400e+00  Time: 0.029,0.144 1773/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [451/469][   920]  Loss: 0.420 (1.20)  Acc: 86.7 (69.5)  LR: 1.000e-03  Grad: 1.980e+00  Time: 0.029,0.145 1770/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [461/469][   930]  Loss: 0.359 (1.19)  Acc: 88.7 (70.0)  LR: 1.000e-03  Grad: 1.912e+00  Time: 0.029,0.145 1771/s  Mem: 3.60,3.85 GB\n",
      "Train:   1 [468/469][   937]  Loss: 0.286 (1.17)  Acc: 93.2 (70.3)  LR: 1.000e-03  Grad: 1.683e+00  Time: 0.029,0.144 1773/s  Mem: 3.60,3.85 GB\n",
      "Val:   1 [  0/20]  Loss: 6.90 (6.90)  Acc: 11.3 (11.3)  Time: 1.006,1.050 244/s  Mem: 0.88,1.36 GB\n",
      "Val:   1 [ 10/20]  Loss: 6.99 (6.69)  Acc: 10.9 (9.45)  Time: 0.171,0.211 1211/s  Mem: 1.23,1.71 GB\n",
      "Val:   1 [ 19/20]  Loss: 7.05 (6.70)  Acc: 9.56 (9.28)  Time: 0.140,0.179 1427/s  Mem: 1.23,1.71 GB\n",
      "Starting epoch 2\n",
      "Train:   2 [  2/469][   940]  Loss: 0.266 (0.290)  Acc: 94.9 (93.1)  LR: 1.000e-03  Grad: 1.472e+00  Time: 0.320,0.436 587/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [ 12/469][   950]  Loss: 0.289 (0.287)  Acc: 94.1 (93.0)  LR: 1.000e-03  Grad: 1.672e+00  Time: 0.105,0.220 1161/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [ 22/469][   960]  Loss: 0.287 (0.299)  Acc: 93.4 (92.8)  LR: 1.000e-03  Grad: 1.740e+00  Time: 0.071,0.187 1372/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [ 32/469][   970]  Loss: 0.336 (0.300)  Acc: 93.8 (92.8)  LR: 1.000e-03  Grad: 1.814e+00  Time: 0.058,0.174 1470/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [ 42/469][   980]  Loss: 0.363 (0.306)  Acc: 89.1 (92.6)  LR: 1.000e-03  Grad: 1.832e+00  Time: 0.051,0.166 1540/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [ 52/469][   990]  Loss: 0.213 (0.299)  Acc: 94.9 (92.7)  LR: 1.000e-03  Grad: 1.431e+00  Time: 0.046,0.162 1585/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [ 62/469][  1000]  Loss: 0.268 (0.296)  Acc: 94.1 (92.8)  LR: 1.000e-03  Grad: 1.586e+00  Time: 0.043,0.159 1614/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [ 72/469][  1010]  Loss: 0.256 (0.296)  Acc: 94.9 (92.7)  LR: 1.000e-03  Grad: 1.422e+00  Time: 0.041,0.156 1638/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [ 82/469][  1020]  Loss: 0.269 (0.291)  Acc: 93.8 (92.9)  LR: 1.000e-03  Grad: 1.482e+00  Time: 0.039,0.155 1656/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [ 92/469][  1030]  Loss: 0.250 (0.290)  Acc: 94.1 (92.9)  LR: 1.000e-03  Grad: 1.725e+00  Time: 0.038,0.153 1670/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [102/469][  1040]  Loss: 0.223 (0.289)  Acc: 95.7 (92.9)  LR: 1.000e-03  Grad: 1.546e+00  Time: 0.036,0.152 1685/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [112/469][  1050]  Loss: 0.244 (0.286)  Acc: 94.1 (93.0)  LR: 1.000e-03  Grad: 1.693e+00  Time: 0.036,0.151 1691/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [122/469][  1060]  Loss: 0.218 (0.285)  Acc: 94.5 (93.0)  LR: 1.000e-03  Grad: 1.653e+00  Time: 0.035,0.150 1702/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [132/469][  1070]  Loss: 0.187 (0.283)  Acc: 96.5 (93.0)  LR: 1.000e-03  Grad: 1.346e+00  Time: 0.034,0.150 1708/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [142/469][  1080]  Loss: 0.295 (0.281)  Acc: 92.6 (93.1)  LR: 1.000e-03  Grad: 1.628e+00  Time: 0.034,0.149 1716/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [152/469][  1090]  Loss: 0.321 (0.281)  Acc: 89.5 (93.0)  LR: 1.000e-03  Grad: 2.230e+00  Time: 0.033,0.149 1722/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [162/469][  1100]  Loss: 0.228 (0.281)  Acc: 96.1 (93.0)  LR: 1.000e-03  Grad: 1.455e+00  Time: 0.033,0.148 1724/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [172/469][  1110]  Loss: 0.229 (0.278)  Acc: 94.5 (93.1)  LR: 1.000e-03  Grad: 1.509e+00  Time: 0.032,0.148 1731/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [182/469][  1120]  Loss: 0.285 (0.277)  Acc: 93.0 (93.1)  LR: 1.000e-03  Grad: 1.489e+00  Time: 0.032,0.148 1735/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [192/469][  1130]  Loss: 0.245 (0.277)  Acc: 94.1 (93.0)  LR: 1.000e-03  Grad: 1.625e+00  Time: 0.031,0.147 1740/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [202/469][  1140]  Loss: 0.230 (0.276)  Acc: 94.5 (93.0)  LR: 1.000e-03  Grad: 1.574e+00  Time: 0.031,0.147 1744/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [212/469][  1150]  Loss: 0.205 (0.274)  Acc: 94.9 (93.1)  LR: 1.000e-03  Grad: 1.461e+00  Time: 0.031,0.147 1747/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [222/469][  1160]  Loss: 0.175 (0.272)  Acc: 96.9 (93.1)  LR: 1.000e-03  Grad: 1.187e+00  Time: 0.031,0.146 1750/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [232/469][  1170]  Loss: 0.294 (0.271)  Acc: 91.4 (93.1)  LR: 1.000e-03  Grad: 1.755e+00  Time: 0.031,0.146 1750/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [242/469][  1180]  Loss: 0.236 (0.270)  Acc: 93.4 (93.1)  LR: 1.000e-03  Grad: 1.679e+00  Time: 0.030,0.146 1753/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [252/469][  1190]  Loss: 0.231 (0.267)  Acc: 94.5 (93.2)  LR: 1.000e-03  Grad: 1.567e+00  Time: 0.030,0.146 1755/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [262/469][  1200]  Loss: 0.154 (0.264)  Acc: 96.5 (93.3)  LR: 1.000e-03  Grad: 1.334e+00  Time: 0.030,0.146 1758/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [272/469][  1210]  Loss: 0.225 (0.262)  Acc: 94.1 (93.3)  LR: 1.000e-03  Grad: 1.399e+00  Time: 0.030,0.145 1760/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [282/469][  1220]  Loss: 0.188 (0.261)  Acc: 95.7 (93.4)  LR: 1.000e-03  Grad: 1.316e+00  Time: 0.030,0.145 1762/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [292/469][  1230]  Loss: 0.150 (0.259)  Acc: 96.9 (93.4)  LR: 1.000e-03  Grad: 1.038e+00  Time: 0.030,0.145 1763/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [302/469][  1240]  Loss: 0.234 (0.257)  Acc: 93.4 (93.4)  LR: 1.000e-03  Grad: 1.312e+00  Time: 0.029,0.145 1765/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [312/469][  1250]  Loss: 0.151 (0.255)  Acc: 95.7 (93.5)  LR: 1.000e-03  Grad: 1.021e+00  Time: 0.029,0.145 1766/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [322/469][  1260]  Loss: 0.363 (0.254)  Acc: 92.6 (93.5)  LR: 1.000e-03  Grad: 1.771e+00  Time: 0.029,0.145 1768/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [332/469][  1270]  Loss: 0.182 (0.252)  Acc: 95.3 (93.6)  LR: 1.000e-03  Grad: 1.387e+00  Time: 0.029,0.145 1769/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [342/469][  1280]  Loss: 0.277 (0.251)  Acc: 92.2 (93.6)  LR: 1.000e-03  Grad: 1.926e+00  Time: 0.029,0.145 1771/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [352/469][  1290]  Loss: 0.205 (0.250)  Acc: 95.7 (93.6)  LR: 1.000e-03  Grad: 1.364e+00  Time: 0.029,0.145 1771/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [362/469][  1300]  Loss: 0.164 (0.248)  Acc: 95.3 (93.7)  LR: 1.000e-03  Grad: 1.436e+00  Time: 0.029,0.144 1773/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [372/469][  1310]  Loss: 0.213 (0.247)  Acc: 93.8 (93.7)  LR: 1.000e-03  Grad: 1.646e+00  Time: 0.029,0.144 1774/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [382/469][  1320]  Loss: 0.171 (0.246)  Acc: 96.1 (93.7)  LR: 1.000e-03  Grad: 1.339e+00  Time: 0.029,0.144 1775/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [392/469][  1330]  Loss: 0.191 (0.244)  Acc: 94.5 (93.8)  LR: 1.000e-03  Grad: 1.343e+00  Time: 0.029,0.144 1775/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [402/469][  1340]  Loss: 0.115 (0.242)  Acc: 96.1 (93.8)  LR: 1.000e-03  Grad: 1.132e+00  Time: 0.028,0.144 1776/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [412/469][  1350]  Loss: 0.176 (0.241)  Acc: 94.9 (93.8)  LR: 1.000e-03  Grad: 1.222e+00  Time: 0.028,0.144 1777/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [422/469][  1360]  Loss: 0.196 (0.239)  Acc: 94.1 (93.9)  LR: 1.000e-03  Grad: 1.346e+00  Time: 0.028,0.144 1778/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [432/469][  1370]  Loss: 0.144 (0.237)  Acc: 96.1 (93.9)  LR: 1.000e-03  Grad: 1.266e+00  Time: 0.028,0.144 1779/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [442/469][  1380]  Loss: 0.158 (0.236)  Acc: 95.3 (94.0)  LR: 1.000e-03  Grad: 1.459e+00  Time: 0.028,0.144 1779/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [452/469][  1390]  Loss: 0.135 (0.235)  Acc: 96.5 (94.0)  LR: 1.000e-03  Grad: 1.180e+00  Time: 0.028,0.144 1779/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [462/469][  1400]  Loss: 0.158 (0.234)  Acc: 95.3 (94.0)  LR: 1.000e-03  Grad: 1.180e+00  Time: 0.028,0.144 1780/s  Mem: 3.60,3.85 GB\n",
      "Train:   2 [468/469][  1406]  Loss: 0.167 (0.233)  Acc: 95.8 (94.0)  LR: 1.000e-03  Grad: 1.297e+00  Time: 0.028,0.144 1781/s  Mem: 3.60,3.85 GB\n",
      "Val:   2 [  0/20]  Loss: 7.68 (7.68)  Acc: 8.20 (8.20)  Time: 0.962,1.003 255/s  Mem: 0.88,1.36 GB\n",
      "Val:   2 [ 10/20]  Loss: 7.75 (7.71)  Acc: 8.20 (7.92)  Time: 0.169,0.209 1224/s  Mem: 1.23,1.71 GB\n",
      "Val:   2 [ 19/20]  Loss: 7.47 (7.69)  Acc: 8.82 (8.28)  Time: 0.138,0.177 1443/s  Mem: 1.23,1.71 GB\n",
      "Done! Run time: 213s\n",
      "*** Final metric: 7.685\n"
     ]
    }
   ],
   "source": [
    "start_time = time.monotonic()\n",
    "\n",
    "for epoch in range(args.epochs):\n",
    "    print(f\"Starting epoch {epoch:d}\")\n",
    "\n",
    "    train_one_epoch(\n",
    "        args=args,\n",
    "        epoch=epoch,\n",
    "        model=model,\n",
    "        task=task,\n",
    "        train_loader=loaders[\"train\"],\n",
    "        optimizer=optimizer,\n",
    "        lr_schedule=lr_schedule,\n",
    "        device=device,\n",
    "    )\n",
    "\n",
    "    metric = validate(\n",
    "        args=args,\n",
    "        epoch=epoch,\n",
    "        model=model,\n",
    "        task=task,\n",
    "        val_loader=loaders[\"validation\"],\n",
    "        device=device,\n",
    "    )\n",
    "\n",
    "    if args.debug:\n",
    "        break\n",
    "\n",
    "print(f\"Done! Run time: {time.monotonic() - start_time:.0f}s\")\n",
    "print(f\"*** Final metric: {metric:.3f}\")"
   ]
  }
 ],
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