{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from fastbook import search_images_ddg, download_images\n",
"\n",
"urls = search_images_ddg(\"doggy\", max_images=5)\n",
"#download_images(\"imgs\", urls=urls)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from fastai.vision.all import *\n",
"import gradio as gr\n",
"\n",
"def is_cat(x): return x[0].isupper()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"PILImage mode=RGB size=192x101"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"im = PILImage.create('imgs/doggy.jpg')\n",
"im.thumbnail((192, 192))\n",
"im\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"learn = load_learner('model.pkl')\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "TypeError",
"evalue": "Exception occured in `ProgressCallback` when calling event `after_batch`:\n\tunsupported format string passed to TensorBase.__format__",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn [4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mlearn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mitem\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mim\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastai/learner.py:303\u001b[0m, in \u001b[0;36mLearner.predict\u001b[0;34m(self, item, rm_type_tfms, with_input)\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, item, rm_type_tfms\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, with_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 302\u001b[0m dl \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdls\u001b[38;5;241m.\u001b[39mtest_dl([item], rm_type_tfms\u001b[38;5;241m=\u001b[39mrm_type_tfms, num_workers\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m--> 303\u001b[0m inp,preds,_,dec_preds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_preds\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwith_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwith_decoded\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 304\u001b[0m i \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdls, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn_inp\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 305\u001b[0m inp \u001b[38;5;241m=\u001b[39m (inp,) \u001b[38;5;28;01mif\u001b[39;00m i\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m tuplify(inp)\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastai/learner.py:290\u001b[0m, in \u001b[0;36mLearner.get_preds\u001b[0;34m(self, ds_idx, dl, with_input, with_decoded, with_loss, act, inner, reorder, cbs, **kwargs)\u001b[0m\n\u001b[1;32m 288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m with_loss: ctx_mgrs\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_not_reduced())\n\u001b[1;32m 289\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ContextManagers(ctx_mgrs):\n\u001b[0;32m--> 290\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_epoch_validate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m act \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: act \u001b[38;5;241m=\u001b[39m getcallable(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_func, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mactivation\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 292\u001b[0m res \u001b[38;5;241m=\u001b[39m cb\u001b[38;5;241m.\u001b[39mall_tensors()\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastai/learner.py:236\u001b[0m, in \u001b[0;36mLearner._do_epoch_validate\u001b[0;34m(self, ds_idx, dl)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dl \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: dl \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdls[ds_idx]\n\u001b[1;32m 235\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdl \u001b[38;5;241m=\u001b[39m dl\n\u001b[0;32m--> 236\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad(): \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_with_events\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mall_batches\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvalidate\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mCancelValidException\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastai/learner.py:193\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_with_events\u001b[39m(\u001b[38;5;28mself\u001b[39m, f, event_type, ex, final\u001b[38;5;241m=\u001b[39mnoop):\n\u001b[0;32m--> 193\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbefore_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m); \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 194\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ex: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_cancel_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 195\u001b[0m \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m); final()\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastai/learner.py:199\u001b[0m, in \u001b[0;36mLearner.all_batches\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mall_batches\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_iter \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdl)\n\u001b[0;32m--> 199\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdl): \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mone_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastai/learner.py:227\u001b[0m, in \u001b[0;36mLearner.one_batch\u001b[0;34m(self, i, b)\u001b[0m\n\u001b[1;32m 225\u001b[0m b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_device(b)\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_split(b)\n\u001b[0;32m--> 227\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_with_events\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_one_batch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbatch\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mCancelBatchException\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastai/learner.py:195\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbefore_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m); f()\n\u001b[1;32m 194\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ex: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_cancel_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 195\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mafter_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mevent_type\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m; final()\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastai/learner.py:171\u001b[0m, in \u001b[0;36mLearner.__call__\u001b[0;34m(self, event_name)\u001b[0m\n\u001b[0;32m--> 171\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, event_name): \u001b[43mL\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent_name\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_one\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastcore/foundation.py:156\u001b[0m, in \u001b[0;36mL.map\u001b[0;34m(self, f, gen, *args, **kwargs)\u001b[0m\n\u001b[0;32m--> 156\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmap\u001b[39m(\u001b[38;5;28mself\u001b[39m, f, \u001b[38;5;241m*\u001b[39margs, gen\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_new(\u001b[43mmap_ex\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgen\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastcore/basics.py:840\u001b[0m, in \u001b[0;36mmap_ex\u001b[0;34m(iterable, f, gen, *args, **kwargs)\u001b[0m\n\u001b[1;32m 838\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(g, iterable)\n\u001b[1;32m 839\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m gen: \u001b[38;5;28;01mreturn\u001b[39;00m res\n\u001b[0;32m--> 840\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mres\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastcore/basics.py:825\u001b[0m, in \u001b[0;36mbind.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 823\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(v,_Arg): kwargs[k] \u001b[38;5;241m=\u001b[39m args\u001b[38;5;241m.\u001b[39mpop(v\u001b[38;5;241m.\u001b[39mi)\n\u001b[1;32m 824\u001b[0m fargs \u001b[38;5;241m=\u001b[39m [args[x\u001b[38;5;241m.\u001b[39mi] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, _Arg) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpargs] \u001b[38;5;241m+\u001b[39m args[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmaxi\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 825\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastai/learner.py:175\u001b[0m, in \u001b[0;36mLearner._call_one\u001b[0;34m(self, event_name)\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_call_one\u001b[39m(\u001b[38;5;28mself\u001b[39m, event_name):\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(event, event_name): \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmissing \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 175\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cb \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcbs\u001b[38;5;241m.\u001b[39msorted(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124morder\u001b[39m\u001b[38;5;124m'\u001b[39m): \u001b[43mcb\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent_name\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastai/callback/core.py:62\u001b[0m, in \u001b[0;36mCallback.__call__\u001b[0;34m(self, event_name)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m: res \u001b[38;5;241m=\u001b[39m getcallable(\u001b[38;5;28mself\u001b[39m, event_name)()\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (CancelBatchException, CancelBackwardException, CancelEpochException, CancelFitException, CancelStepException, CancelTrainException, CancelValidException): \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[0;32m---> 62\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e: \u001b[38;5;28;01mraise\u001b[39;00m modify_exception(e, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mException occured in `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\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` when calling event `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m`:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;241m.\u001b[39margs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m, replace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m event_name\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_fit\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m \u001b[38;5;66;03m#Reset self.run to True at each end of fit\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastai/callback/core.py:60\u001b[0m, in \u001b[0;36mCallback.__call__\u001b[0;34m(self, event_name)\u001b[0m\n\u001b[1;32m 58\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun \u001b[38;5;129;01mand\u001b[39;00m _run: \n\u001b[0;32m---> 60\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m: res \u001b[38;5;241m=\u001b[39m \u001b[43mgetcallable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent_name\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (CancelBatchException, CancelBackwardException, CancelEpochException, CancelFitException, CancelStepException, CancelTrainException, CancelValidException): \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e: \u001b[38;5;28;01mraise\u001b[39;00m modify_exception(e, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mException occured in `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\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` when calling event `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m`:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;241m.\u001b[39margs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m, replace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastai/callback/progress.py:33\u001b[0m, in \u001b[0;36mProgressCallback.after_batch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mafter_batch\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpbar\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miter\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m---> 33\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msmooth_loss\u001b[39m\u001b[38;5;124m'\u001b[39m): \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpbar\u001b[38;5;241m.\u001b[39mcomment \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msmooth_loss\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/torch/_tensor.py:855\u001b[0m, in \u001b[0;36mTensor.__format__\u001b[0;34m(self, format_spec)\u001b[0m\n\u001b[1;32m 853\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__format__\u001b[39m(\u001b[38;5;28mself\u001b[39m, format_spec):\n\u001b[1;32m 854\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 855\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mhandle_torch_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mTensor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__format__\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformat_spec\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 856\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdim() \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_meta \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m Tensor:\n\u001b[1;32m 857\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__format__\u001b[39m(format_spec)\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/torch/overrides.py:1534\u001b[0m, in \u001b[0;36mhandle_torch_function\u001b[0;34m(public_api, relevant_args, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1528\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDefining your `__torch_function__ as a plain method is deprecated and \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwill be an error in future, please define it as a classmethod.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m)\n\u001b[1;32m 1532\u001b[0m \u001b[38;5;66;03m# Use `public_api` instead of `implementation` so __torch_function__\u001b[39;00m\n\u001b[1;32m 1533\u001b[0m \u001b[38;5;66;03m# implementations can do equality/identity comparisons.\u001b[39;00m\n\u001b[0;32m-> 1534\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mtorch_func_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpublic_api\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/fastai/torch_core.py:376\u001b[0m, in \u001b[0;36mTensorBase.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 374\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mdebug \u001b[38;5;129;01mand\u001b[39;00m func\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__str__\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__repr__\u001b[39m\u001b[38;5;124m'\u001b[39m): \u001b[38;5;28mprint\u001b[39m(func, types, args, kwargs)\n\u001b[1;32m 375\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _torch_handled(args, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_opt, func): types \u001b[38;5;241m=\u001b[39m (torch\u001b[38;5;241m.\u001b[39mTensor,)\n\u001b[0;32m--> 376\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__torch_function__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mifnone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 377\u001b[0m dict_objs \u001b[38;5;241m=\u001b[39m _find_args(args) \u001b[38;5;28;01mif\u001b[39;00m args \u001b[38;5;28;01melse\u001b[39;00m _find_args(\u001b[38;5;28mlist\u001b[39m(kwargs\u001b[38;5;241m.\u001b[39mvalues()))\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mtype\u001b[39m(res),TensorBase) \u001b[38;5;129;01mand\u001b[39;00m dict_objs: res\u001b[38;5;241m.\u001b[39mset_meta(dict_objs[\u001b[38;5;241m0\u001b[39m],as_copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/torch/_tensor.py:1278\u001b[0m, in \u001b[0;36mTensor.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 1275\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m 1277\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _C\u001b[38;5;241m.\u001b[39mDisableTorchFunction():\n\u001b[0;32m-> 1278\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1279\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01min\u001b[39;00m get_default_nowrap_functions():\n\u001b[1;32m 1280\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
"File \u001b[0;32m~/mambaforge/envs/fastbook/lib/python3.10/site-packages/torch/_tensor.py:858\u001b[0m, in \u001b[0;36mTensor.__format__\u001b[0;34m(self, format_spec)\u001b[0m\n\u001b[1;32m 856\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdim() \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_meta \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m Tensor:\n\u001b[1;32m 857\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__format__\u001b[39m(format_spec)\n\u001b[0;32m--> 858\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__format__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformat_spec\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mTypeError\u001b[0m: Exception occured in `ProgressCallback` when calling event `after_batch`:\n\tunsupported format string passed to TensorBase.__format__"
]
}
],
"source": [
"\n",
"learn.predict(item=im)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.6"
},
"vscode": {
"interpreter": {
"hash": "9c3056ad522d1101aacd22903a5d3f3e2a1ecfe656a42b1e5d6b9369fabf2456"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}