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update tutorial
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@inoue0426
inoue0426 committed Nov 7, 2024
1 parent 61d336a commit 4d49d7f
Showing 1 changed file with 79 additions and 48 deletions.
127 changes: 79 additions & 48 deletions Tutorial.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -37,16 +37,7 @@
"execution_count": 3,
"id": "869740ff-e2fc-43b8-9a0f-49e637522ec4",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/y3/ssnk1ytd3m5bjmrchh2lt74srg76p8/T/ipykernel_84689/2505546517.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
" test = torch.load(\"test.pt\")\n"
]
}
],
"outputs": [],
"source": [
"test = torch.load(\"test.pt\")"
]
Expand All @@ -58,52 +49,100 @@
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/y3/ssnk1ytd3m5bjmrchh2lt74srg76p8/T/ipykernel_84689/3516971570.py:3: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
" model = torch.load(\"sample.pt\", map_location=device)\n"
]
},
{
"ename": "AttributeError",
"evalue": "'Linear' object has no attribute '_lazy_load_hook'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[4], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m tmp \u001b[38;5;241m=\u001b[39m get_ipython()\u001b[38;5;241m.\u001b[39mgetoutput(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mls | grep pt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 2\u001b[0m device \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mis_available() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msample.pt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniconda3/envs/torch/lib/python3.10/site-packages/torch/serialization.py:1360\u001b[0m, in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[0m\n\u001b[1;32m 1358\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m pickle\u001b[38;5;241m.\u001b[39mUnpicklingError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 1359\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m pickle\u001b[38;5;241m.\u001b[39mUnpicklingError(_get_wo_message(\u001b[38;5;28mstr\u001b[39m(e))) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1360\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_load\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1361\u001b[0m \u001b[43m \u001b[49m\u001b[43mopened_zipfile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1362\u001b[0m \u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1363\u001b[0m \u001b[43m \u001b[49m\u001b[43mpickle_module\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1364\u001b[0m \u001b[43m \u001b[49m\u001b[43moverall_storage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverall_storage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1365\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpickle_load_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1366\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1367\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mmap:\n\u001b[1;32m 1368\u001b[0m f_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(f, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mf\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
"File \u001b[0;32m~/miniconda3/envs/torch/lib/python3.10/site-packages/torch/serialization.py:1848\u001b[0m, in \u001b[0;36m_load\u001b[0;34m(zip_file, map_location, pickle_module, pickle_file, overall_storage, **pickle_load_args)\u001b[0m\n\u001b[1;32m 1846\u001b[0m \u001b[38;5;28;01mglobal\u001b[39;00m _serialization_tls\n\u001b[1;32m 1847\u001b[0m _serialization_tls\u001b[38;5;241m.\u001b[39mmap_location \u001b[38;5;241m=\u001b[39m map_location\n\u001b[0;32m-> 1848\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43munpickler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1849\u001b[0m _serialization_tls\u001b[38;5;241m.\u001b[39mmap_location \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1851\u001b[0m torch\u001b[38;5;241m.\u001b[39m_utils\u001b[38;5;241m.\u001b[39m_validate_loaded_sparse_tensors()\n",
"File \u001b[0;32m~/miniconda3/envs/torch/lib/python3.10/site-packages/torch/nn/modules/module.py:1931\u001b[0m, in \u001b[0;36mModule.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m modules:\n\u001b[1;32m 1930\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m modules[name]\n\u001b[0;32m-> 1931\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 1932\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1933\u001b[0m )\n",
"\u001b[0;31mAttributeError\u001b[0m: 'Linear' object has no attribute '_lazy_load_hook'"
]
}
],
"outputs": [],
"source": [
"tmp = !ls | grep pt\n",
"model = torch.load(\"sample.pt\", map_location=device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"id": "subject-allen",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>Accuracy</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1 Score</th>\n",
" <th>True Positive</th>\n",
" <th>True Negative</th>\n",
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"text/plain": [
" Accuracy Precision Recall F1 Score True Positive True Negative \\\n",
"0 0.771375 0.740881 0.783245 0.761474 1178 1312 \n",
"\n",
" False Positive False Negative \n",
"0 412 326 "
]
},
"execution_count": 5,
"metadata": {},
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],
"source": [
"predict, res = drGAT.eval(model, test)\n",
"res"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"id": "korean-recipe",
"metadata": {},
"outputs": [],
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"tensor([1., 0., 0., ..., 1., 0., 0.])"
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"execution_count": 6,
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],
"source": [
"predict"
]
Expand All @@ -115,21 +154,13 @@
"metadata": {},
"outputs": [],
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},
{
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