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Add EmoSet training and evaluation

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zin
2026-01-13 10:00:04 +00:00
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"text": [
"Requirement already satisfied: datasets in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (4.4.2)\n",
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"!pip install datasets tqdm pillow requests\n"
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"text": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['image', 'label', 'image_id', 'emotion', 'brightness', 'colorfulness', 'facial_expression', 'human_action', 'scene', 'object'],\n",
" num_rows: 94481\n",
" })\n",
" val: Dataset({\n",
" features: ['image', 'label', 'image_id', 'emotion', 'brightness', 'colorfulness', 'facial_expression', 'human_action', 'scene', 'object'],\n",
" num_rows: 5905\n",
" })\n",
" test: Dataset({\n",
" features: ['image', 'label', 'image_id', 'emotion', 'brightness', 'colorfulness', 'facial_expression', 'human_action', 'scene', 'object'],\n",
" num_rows: 17716\n",
" })\n",
"})\n"
]
}
],
"source": [
"from datasets import load_dataset\n",
"from pathlib import Path\n",
"from PIL import Image\n",
"import requests\n",
"\n",
"# куда сохраняем датасет\n",
"DATA_DIR = Path(\"../dataset/EmoSet-118K\")\n",
"DATA_DIR.mkdir(exist_ok=True, parents=True)\n",
"\n",
"# загружаем через Hugging Face\n",
"ds = load_dataset(\"Woleek/EmoSet-118K\")\n",
"\n",
"print(ds)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "052ab073",
"metadata": {},
"outputs": [],
"source": [
"from tqdm import tqdm\n",
"from pathlib import Path\n",
"\n",
"def save_split(split):\n",
" split_dir = DATA_DIR / split\n",
" img_dir = split_dir / \"images\"\n",
" img_dir.mkdir(parents=True, exist_ok=True)\n",
"\n",
" labels_path = split_dir / \"labels.csv\"\n",
"\n",
" # перезаписываем labels.csv\n",
" with open(labels_path, \"w\") as f:\n",
" f.write(\"filename,label\\n\")\n",
"\n",
" for example in tqdm(ds[split]):\n",
" img = example[\"image\"] # уже PIL.Image\n",
" label = example[\"emotion\"]\n",
" image_id = example[\"image_id\"]\n",
"\n",
" fname = f\"{image_id}.jpg\"\n",
" img.save(img_dir / fname)\n",
"\n",
" with open(labels_path, \"a\") as f:\n",
" f.write(f\"{fname},{label}\\n\")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a74ceedf",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 94481/94481 [18:43<00:00, 84.10it/s] \n",
"100%|██████████| 5905/5905 [01:08<00:00, 86.57it/s] \n",
"100%|██████████| 17716/17716 [02:57<00:00, 100.01it/s]\n"
]
}
],
"source": [
"save_split(\"train\")\n",
"save_split(\"val\")\n",
"save_split(\"test\")\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "thesis-py3.11",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "d70d8e32",
"metadata": {},
"outputs": [],
"source": [
"from concurrent.futures import ProcessPoolExecutor\n",
"import pandas as pd\n",
"from pathlib import Path\n",
"from PIL import Image\n",
"import torch\n",
"from torchvision import transforms\n",
"from tqdm import tqdm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "31b0fa82",
"metadata": {},
"outputs": [],
"source": [
"DATA_ROOT = Path(\"../dataset/EmoSet-118K\")\n",
"TRANSFORM = transforms.Compose([\n",
" transforms.Resize((224,224)),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1a17ecf5",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/94481 [00:00<?, ?it/s]\n"
]
},
{
"ename": "PicklingError",
"evalue": "Can't pickle <class 'pandas.core.frame.Pandas'>: attribute lookup Pandas on pandas.core.frame failed",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31m_RemoteTraceback\u001b[39m Traceback (most recent call last)",
"\u001b[31m_RemoteTraceback\u001b[39m: \n\"\"\"\nTraceback (most recent call last):\n File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 244, in _feed\n obj = _ForkingPickler.dumps(obj)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/reduction.py\", line 51, in dumps\n cls(buf, protocol).dump(obj)\n_pickle.PicklingError: Can't pickle <class 'pandas.core.frame.Pandas'>: attribute lookup Pandas on pandas.core.frame failed\n\"\"\"",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[31mPicklingError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 20\u001b[39m\n\u001b[32m 18\u001b[39m futures = [executor.submit(process_row, row, split_dir, tensor_dir) \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m df.itertuples()]\n\u001b[32m 19\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m tqdm(futures):\n\u001b[32m---> \u001b[39m\u001b[32m20\u001b[39m results.append(\u001b[43mf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m 22\u001b[39m new_df = pd.DataFrame(results)\n\u001b[32m 23\u001b[39m new_df.to_csv(DATA_ROOT / split / \u001b[33m\"\u001b[39m\u001b[33mlabels_tensor.csv\u001b[39m\u001b[33m\"\u001b[39m, index=\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:449\u001b[39m, in \u001b[36mFuture.result\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 447\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[32m 448\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state == FINISHED:\n\u001b[32m--> \u001b[39m\u001b[32m449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 451\u001b[39m \u001b[38;5;28mself\u001b[39m._condition.wait(timeout)\n\u001b[32m 453\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:401\u001b[39m, in \u001b[36mFuture.__get_result\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 399\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception:\n\u001b[32m 400\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m401\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception\n\u001b[32m 402\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 403\u001b[39m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[32m 404\u001b[39m \u001b[38;5;28mself\u001b[39m = \u001b[38;5;28;01mNone\u001b[39;00m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py:244\u001b[39m, in \u001b[36mQueue._feed\u001b[39m\u001b[34m(buffer, notempty, send_bytes, writelock, reader_close, writer_close, ignore_epipe, onerror, queue_sem)\u001b[39m\n\u001b[32m 241\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m 243\u001b[39m \u001b[38;5;66;03m# serialize the data before acquiring the lock\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m244\u001b[39m obj = \u001b[43m_ForkingPickler\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdumps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 245\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m wacquire \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 246\u001b[39m send_bytes(obj)\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/reduction.py:51\u001b[39m, in \u001b[36mForkingPickler.dumps\u001b[39m\u001b[34m(cls, obj, protocol)\u001b[39m\n\u001b[32m 48\u001b[39m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[32m 49\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdumps\u001b[39m(\u001b[38;5;28mcls\u001b[39m, obj, protocol=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 50\u001b[39m buf = io.BytesIO()\n\u001b[32m---> \u001b[39m\u001b[32m51\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbuf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 52\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m buf.getbuffer()\n",
"\u001b[31mPicklingError\u001b[39m: Can't pickle <class 'pandas.core.frame.Pandas'>: attribute lookup Pandas on pandas.core.frame failed"
]
}
],
"source": [
"def process_row(row, split_dir, tensor_dir):\n",
" img_path = split_dir / row.filename\n",
" img = Image.open(img_path).convert(\"RGB\")\n",
" tensor = TRANSFORM(img)\n",
" tensor_path = tensor_dir / f\"{row.filename}.pt\"\n",
" torch.save(tensor, tensor_path)\n",
" return {\"tensor_path\": str(tensor_path), \"label\": row.label}\n",
"\n",
"for split in [\"train\",\"val\",\"test\"]:\n",
" split_dir = DATA_ROOT / split / \"images\"\n",
" tensor_dir = DATA_ROOT / split / \"tensors\"\n",
" tensor_dir.mkdir(exist_ok=True, parents=True)\n",
"\n",
" df = pd.read_csv(DATA_ROOT / split / \"labels.csv\")\n",
"\n",
" results = []\n",
" with ProcessPoolExecutor(max_workers=12) as executor:\n",
" futures = [executor.submit(process_row, row, split_dir, tensor_dir) for row in df.itertuples()]\n",
" for f in tqdm(futures):\n",
" results.append(f.result())\n",
"\n",
" new_df = pd.DataFrame(results)\n",
" new_df.to_csv(DATA_ROOT / split / \"labels_tensor.csv\", index=False)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "thesis-py3.11",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "ca08df84",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using device: cuda\n",
"Step 0/1000, Loss: 1.0042\n",
"Step 10/1000, Loss: 1.0012\n",
"Step 20/1000, Loss: 0.9928\n",
"Step 30/1000, Loss: 0.9793\n",
"Step 40/1000, Loss: 0.9584\n",
"Step 50/1000, Loss: 0.9277\n",
"Step 60/1000, Loss: 0.8837\n",
"Step 70/1000, Loss: 0.8089\n",
"Step 80/1000, Loss: 0.7500\n",
"Step 90/1000, Loss: 0.6431\n",
"Step 100/1000, Loss: 0.5583\n",
"Step 110/1000, Loss: 0.4861\n",
"Step 120/1000, Loss: 0.4373\n",
"Step 130/1000, Loss: 0.3796\n",
"Step 140/1000, Loss: 0.3441\n",
"Step 150/1000, Loss: 0.3014\n",
"Step 160/1000, Loss: 0.2682\n",
"Step 170/1000, Loss: 0.2513\n",
"Step 180/1000, Loss: 0.2077\n",
"Step 190/1000, Loss: 0.1861\n",
"Step 200/1000, Loss: 0.1674\n",
"Step 210/1000, Loss: 0.1552\n",
"Step 220/1000, Loss: 0.1284\n",
"Step 230/1000, Loss: 0.1103\n",
"Step 240/1000, Loss: 0.1114\n",
"Step 250/1000, Loss: 0.0993\n",
"Step 260/1000, Loss: 0.0870\n",
"Step 270/1000, Loss: 0.1048\n",
"Step 280/1000, Loss: 0.0787\n",
"Step 290/1000, Loss: 0.0666\n",
"Step 300/1000, Loss: 0.0603\n",
"Step 310/1000, Loss: 0.0668\n",
"Step 320/1000, Loss: 0.0466\n",
"Step 330/1000, Loss: 0.0398\n",
"Step 340/1000, Loss: 0.0421\n",
"Step 350/1000, Loss: 0.0494\n",
"Step 360/1000, Loss: 0.0381\n",
"Step 370/1000, Loss: 0.0329\n",
"Step 380/1000, Loss: 0.0529\n",
"Step 390/1000, Loss: 0.0306\n",
"Step 400/1000, Loss: 0.0257\n",
"Step 410/1000, Loss: 0.0292\n",
"Step 420/1000, Loss: 0.0260\n",
"Step 430/1000, Loss: 0.0225\n",
"Step 440/1000, Loss: 0.0262\n",
"Step 450/1000, Loss: 0.0220\n",
"Step 460/1000, Loss: 0.0206\n",
"Step 470/1000, Loss: 0.0354\n",
"Step 480/1000, Loss: 0.0271\n",
"Step 490/1000, Loss: 0.0214\n",
"Step 500/1000, Loss: 0.0186\n",
"Step 510/1000, Loss: 0.0153\n",
"Step 520/1000, Loss: 0.0141\n",
"Step 530/1000, Loss: 0.0349\n",
"Step 540/1000, Loss: 0.0186\n",
"Step 550/1000, Loss: 0.0157\n",
"Step 560/1000, Loss: 0.0142\n",
"Step 570/1000, Loss: 0.0345\n",
"Step 580/1000, Loss: 0.0166\n",
"Step 590/1000, Loss: 0.0156\n",
"Step 600/1000, Loss: 0.0136\n",
"Step 610/1000, Loss: 0.0434\n",
"Step 620/1000, Loss: 0.0210\n",
"Step 630/1000, Loss: 0.0170\n",
"Step 640/1000, Loss: 0.0177\n",
"Step 650/1000, Loss: 0.0312\n",
"Step 660/1000, Loss: 0.0219\n",
"Step 670/1000, Loss: 0.0169\n",
"Step 680/1000, Loss: 0.0149\n",
"Step 690/1000, Loss: 0.0172\n",
"Step 700/1000, Loss: 0.0131\n",
"Step 710/1000, Loss: 0.0257\n",
"Step 720/1000, Loss: 0.0158\n",
"Step 730/1000, Loss: 0.0152\n",
"Step 740/1000, Loss: 0.0166\n",
"Step 750/1000, Loss: 0.0301\n",
"Step 760/1000, Loss: 0.0187\n",
"Step 770/1000, Loss: 0.0179\n",
"Step 780/1000, Loss: 0.0177\n",
"Step 790/1000, Loss: 0.0168\n",
"Step 800/1000, Loss: 0.0297\n",
"Step 810/1000, Loss: 0.0182\n",
"Step 820/1000, Loss: 0.0158\n",
"Step 830/1000, Loss: 0.0143\n",
"Step 840/1000, Loss: 0.0145\n",
"Step 850/1000, Loss: 0.0175\n",
"Step 860/1000, Loss: 0.0200\n",
"Step 870/1000, Loss: 0.0157\n",
"Step 880/1000, Loss: 0.0133\n",
"Step 890/1000, Loss: 0.0159\n",
"Step 900/1000, Loss: 0.0190\n",
"Step 910/1000, Loss: 0.0146\n",
"Step 920/1000, Loss: 0.0132\n",
"Step 930/1000, Loss: 0.0161\n",
"Step 940/1000, Loss: 0.0134\n",
"Step 950/1000, Loss: 0.0249\n",
"Step 960/1000, Loss: 0.0146\n",
"Step 970/1000, Loss: 0.0116\n",
"Step 980/1000, Loss: 0.0136\n",
"Step 990/1000, Loss: 0.0133\n",
"Total time: 66.18 s\n"
]
}
],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"import time\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"print(\"Using device:\", device)\n",
"\n",
"# Огромные параметры\n",
"N, D_in, H1, H2, H3, D_out = 300_000, 4096, 2048, 1024, 512, 10\n",
"batch_size = 16_384 # большой батч\n",
"steps = 1000 # много итераций для длительной нагрузки\n",
"\n",
"# Случайные данные на GPU\n",
"x = torch.randn(N, D_in, device=device, dtype=torch.float32)\n",
"y = torch.randn(N, D_out, device=device, dtype=torch.float32)\n",
"\n",
"model = nn.Sequential(\n",
" nn.Linear(D_in, H1),\n",
" nn.ReLU(),\n",
" nn.Linear(H1, H2),\n",
" nn.ReLU(),\n",
" nn.Linear(H2, H3),\n",
" nn.ReLU(),\n",
" nn.Linear(H3, D_out)\n",
").to(device)\n",
"\n",
"loss_fn = nn.MSELoss()\n",
"optimizer = optim.Adam(model.parameters(), lr=1e-3)\n",
"\n",
"start = time.time()\n",
"for t in range(steps):\n",
" idx = torch.randint(0, N, (batch_size,), device=device)\n",
" x_batch = x[idx]\n",
" y_batch = y[idx]\n",
"\n",
" y_pred = model(x_batch)\n",
" loss = loss_fn(y_pred, y_batch)\n",
"\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" if t % 10 == 0:\n",
" # замедляем вывод, чтобы можно было наблюдать\n",
" print(f\"Step {t}/{steps}, Loss: {loss.item():.4f}\")\n",
"\n",
"end = time.time()\n",
"print(f\"Total time: {end-start:.2f} s\")\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (my-python-project)",
"language": "python",
"name": "my-python-project"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "9336560f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0c00b67b",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"from torch.utils.data import Dataset, DataLoader\n",
"import torchvision.transforms as T\n",
"\n",
"import pandas as pd\n",
"from pathlib import Path\n",
"from PIL import Image\n",
"from tqdm import tqdm\n",
"\n",
"import timm\n",
"import numpy as np\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "84c3657f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'cuda'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# === CONFIG ===\n",
"DATA_ROOT = Path(\"../dataset/EmoSet-118K\")\n",
"BATCH_SIZE = 64 # V100 спокойно тянет\n",
"EPOCHS = 15\n",
"LR = 3e-4\n",
"NUM_WORKERS = 24\n",
"\n",
"DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"DEVICE\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9f749add",
"metadata": {},
"outputs": [],
"source": [
"class EmoSetDataset(Dataset):\n",
" def __init__(self, root, split):\n",
" self.root = Path(root) / split\n",
" self.df = pd.read_csv(self.root / \"labels.csv\")\n",
"\n",
" self.labels = sorted(self.df[\"label\"].unique())\n",
" self.label2idx = {l: i for i, l in enumerate(self.labels)}\n",
" self.idx2label = {i: l for l, i in self.label2idx.items()}\n",
"\n",
" self.transform = T.Compose([\n",
" T.Resize((224, 224)),\n",
" T.ToTensor(),\n",
" T.Normalize(\n",
" mean=[0.485, 0.456, 0.406],\n",
" std=[0.229, 0.224, 0.225]\n",
" )\n",
" ])\n",
"\n",
" def __len__(self):\n",
" return len(self.df)\n",
"\n",
" def __getitem__(self, idx):\n",
" row = self.df.iloc[idx]\n",
" img_path = self.root / \"images\" / row[\"filename\"]\n",
"\n",
" img = Image.open(img_path).convert(\"RGB\")\n",
" img = self.transform(img)\n",
"\n",
" label = self.label2idx[row[\"label\"]]\n",
" return img, label\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c8805341",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Classes: ['amusement', 'anger', 'awe', 'contentment', 'disgust', 'excitement', 'fear', 'sadness']\n"
]
}
],
"source": [
"train_ds = EmoSetDataset(DATA_ROOT, \"train\")\n",
"val_ds = EmoSetDataset(DATA_ROOT, \"val\")\n",
"\n",
"train_loader = DataLoader(\n",
" train_ds,\n",
" batch_size=BATCH_SIZE,\n",
" shuffle=True,\n",
" num_workers=NUM_WORKERS,\n",
" pin_memory=True\n",
")\n",
"\n",
"val_loader = DataLoader(\n",
" val_ds,\n",
" batch_size=BATCH_SIZE,\n",
" shuffle=False,\n",
" num_workers=NUM_WORKERS,\n",
" pin_memory=True\n",
")\n",
"\n",
"print(\"Classes:\", train_ds.labels)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "dffce582",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ResNet(\n",
" (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
" (layer1): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (layer2): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" )\n",
" (3): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (layer3): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" )\n",
" (3): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" )\n",
" (4): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" )\n",
" (5): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (layer4): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act1): ReLU(inplace=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (drop_block): Identity()\n",
" (act2): ReLU(inplace=True)\n",
" (aa): Identity()\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (act3): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
" (fc): Linear(in_features=2048, out_features=8, bias=True)\n",
")"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = timm.create_model(\n",
" \"resnet50\",\n",
" pretrained=True,\n",
" num_classes=len(train_ds.labels)\n",
")\n",
"\n",
"model.to(DEVICE)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "81a457ef",
"metadata": {},
"outputs": [],
"source": [
"criterion = nn.CrossEntropyLoss()\n",
"\n",
"optimizer = torch.optim.AdamW(\n",
" model.parameters(),\n",
" lr=LR,\n",
" weight_decay=1e-4\n",
")\n",
"\n",
"scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
" optimizer,\n",
" T_max=EPOCHS\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "951aa9e3",
"metadata": {},
"outputs": [],
"source": [
"def train_epoch(model, loader):\n",
" model.train()\n",
" total_loss = 0\n",
" correct = 0\n",
" total = 0\n",
"\n",
" for imgs, labels in tqdm(loader, leave=False):\n",
" imgs = imgs.to(DEVICE)\n",
" labels = labels.to(DEVICE)\n",
"\n",
" optimizer.zero_grad()\n",
" logits = model(imgs)\n",
" loss = criterion(logits, labels)\n",
"\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" total_loss += loss.item() * imgs.size(0)\n",
" preds = logits.argmax(dim=1)\n",
" correct += (preds == labels).sum().item()\n",
" total += labels.size(0)\n",
"\n",
" return total_loss / total, correct / total\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "fb7e9398",
"metadata": {},
"outputs": [],
"source": [
"@torch.no_grad()\n",
"def val_epoch(model, loader):\n",
" model.eval()\n",
" total_loss = 0\n",
" correct = 0\n",
" total = 0\n",
"\n",
" for imgs, labels in loader:\n",
" imgs = imgs.to(DEVICE)\n",
" labels = labels.to(DEVICE)\n",
"\n",
" logits = model(imgs)\n",
" loss = criterion(logits, labels)\n",
"\n",
" total_loss += loss.item() * imgs.size(0)\n",
" preds = logits.argmax(dim=1)\n",
" correct += (preds == labels).sum().item()\n",
" total += labels.size(0)\n",
"\n",
" return total_loss / total, correct / total\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "9e870e5d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/1477 [00:00<?, ?it/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 01 | Train loss: 0.8383, acc: 0.6954 | Val loss: 0.6694, acc: 0.7563\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 02 | Train loss: 0.5462, acc: 0.7972 | Val loss: 0.6592, acc: 0.7594\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 03 | Train loss: 0.3654, acc: 0.8632 | Val loss: 0.7263, acc: 0.7600\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 04 | Train loss: 0.2111, acc: 0.9230 | Val loss: 0.8572, acc: 0.7472\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 05 | Train loss: 0.1187, acc: 0.9585 | Val loss: 1.0372, acc: 0.7453\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 06 | Train loss: 0.0690, acc: 0.9768 | Val loss: 1.1982, acc: 0.7529\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 07 | Train loss: 0.0466, acc: 0.9843 | Val loss: 1.3178, acc: 0.7492\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 08 | Train loss: 0.0295, acc: 0.9905 | Val loss: 1.3926, acc: 0.7551\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 09 | Train loss: 0.0204, acc: 0.9938 | Val loss: 1.4682, acc: 0.7497\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 10 | Train loss: 0.0146, acc: 0.9955 | Val loss: 1.4784, acc: 0.7604\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 11 | Train loss: 0.0087, acc: 0.9975 | Val loss: 1.5263, acc: 0.7580\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 12 | Train loss: 0.0057, acc: 0.9987 | Val loss: 1.5689, acc: 0.7558\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 13 | Train loss: 0.0044, acc: 0.9990 | Val loss: 1.5952, acc: 0.7566\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 14 | Train loss: 0.0030, acc: 0.9993 | Val loss: 1.6130, acc: 0.7600\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 15 | Train loss: 0.0025, acc: 0.9995 | Val loss: 1.5921, acc: 0.7627\n"
]
}
],
"source": [
"best_val_acc = 0.0\n",
"\n",
"for epoch in range(1, EPOCHS + 1):\n",
" train_loss, train_acc = train_epoch(model, train_loader)\n",
" val_loss, val_acc = val_epoch(model, val_loader)\n",
"\n",
" scheduler.step()\n",
"\n",
" print(\n",
" f\"Epoch {epoch:02d} | \"\n",
" f\"Train loss: {train_loss:.4f}, acc: {train_acc:.4f} | \"\n",
" f\"Val loss: {val_loss:.4f}, acc: {val_acc:.4f}\"\n",
" )\n",
"\n",
" if val_acc > best_val_acc:\n",
" best_val_acc = val_acc\n",
" torch.save(model.state_dict(), \"emoset_resnet50_best.pth\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7796ef11",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "thesis-py3.11",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}