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02-saving-a-basic-fastai-model.ipynb

@@ -0,0 +1,216 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "98d53c05"
+   },
+   "source": [
+    "## Saving a Cats v Dogs Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a minimal example showing how to train a fastai model on Kaggle, and save it so you can use it in your app."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "_kg_hide-input": true,
+    "_kg_hide-output": true,
+    "execution": {
+     "iopub.execute_input": "2022-05-03T05:51:37.949032Z",
+     "iopub.status.busy": "2022-05-03T05:51:37.948558Z",
+     "iopub.status.idle": "2022-05-03T05:51:59.531217Z",
+     "shell.execute_reply": "2022-05-03T05:51:59.530294Z",
+     "shell.execute_reply.started": "2022-05-03T05:51:37.948947Z"
+    },
+    "id": "evvA0fqvSblq",
+    "outputId": "ba21b811-767c-459a-ccdf-044758720a55"
+   },
+   "outputs": [],
+   "source": [
+    "# Make sure we've got the latest version of fastai:\n",
+    "!pip install -Uqq fastai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, import all the stuff we need from fastai:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-05-03T05:51:59.534478Z",
+     "iopub.status.busy": "2022-05-03T05:51:59.533878Z",
+     "iopub.status.idle": "2022-05-03T05:52:02.177975Z",
+     "shell.execute_reply": "2022-05-03T05:52:02.177267Z",
+     "shell.execute_reply.started": "2022-05-03T05:51:59.534432Z"
+    },
+    "id": "44eb0ad3"
+   },
+   "outputs": [],
+   "source": [
+    "from fastai.vision.all import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Download and decompress our dataset, which is pictures of dogs and cats:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-05-03T05:52:02.180691Z",
+     "iopub.status.busy": "2022-05-03T05:52:02.180192Z",
+     "iopub.status.idle": "2022-05-03T05:53:02.465242Z",
+     "shell.execute_reply": "2022-05-03T05:53:02.464516Z",
+     "shell.execute_reply.started": "2022-05-03T05:52:02.180651Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "path = untar_data(URLs.PETS)/'images'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need a way to label our images as dogs or cats. In this dataset, pictures of cats are given a filename that starts with a capital letter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-05-03T05:53:02.467572Z",
+     "iopub.status.busy": "2022-05-03T05:53:02.467289Z",
+     "iopub.status.idle": "2022-05-03T05:53:02.474701Z",
+     "shell.execute_reply": "2022-05-03T05:53:02.474109Z",
+     "shell.execute_reply.started": "2022-05-03T05:53:02.467536Z"
+    },
+    "id": "44eb0ad3"
+   },
+   "outputs": [],
+   "source": [
+    "def is_cat(x): return x[0].isupper() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create our `DataLoaders`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-05-03T05:53:02.476084Z",
+     "iopub.status.busy": "2022-05-03T05:53:02.475754Z",
+     "iopub.status.idle": "2022-05-03T05:53:06.703777Z",
+     "shell.execute_reply": "2022-05-03T05:53:06.703023Z",
+     "shell.execute_reply.started": "2022-05-03T05:53:02.476052Z"
+    },
+    "id": "44eb0ad3"
+   },
+   "outputs": [],
+   "source": [
+    "dls = ImageDataLoaders.from_name_func('.',\n",
+    "    get_image_files(path), valid_pct=0.2, seed=42,\n",
+    "    label_func=is_cat,\n",
+    "    item_tfms=Resize(192))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... and train our model, a resnet18 (to keep it small and fast):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-05-03T05:53:28.093059Z",
+     "iopub.status.busy": "2022-05-03T05:53:28.092381Z"
+    },
+    "id": "c107f724",
+    "outputId": "fcc1de68-7c8b-43f5-b9eb-fcdb0773ef07"
+   },
+   "outputs": [],
+   "source": [
+    "learn = vision_learner(dls, resnet18, metrics=error_rate)\n",
+    "learn.fine_tune(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can export our trained `Learner`. This contains all the information needed to run the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ae2bc6ac"
+   },
+   "outputs": [],
+   "source": [
+    "learn.export('model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Q2HTrQKTf3BV"
+   },
+   "source": [
+    "Finally, open the Kaggle sidebar on the right if it's not already, and find the section marked \"Output\". Open the `/kaggle/working` folder, and you'll see `model.pkl`. Click on it, then click on the menu on the right that appears, and choose \"Download\". After a few seconds, your model will be downloaded to your computer, where you can then create your app that uses the model."
+   ]
+  }
+ ],
+ "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": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

README.md

@@ -1,12 +1,6 @@
 ---
-title: Simple Classifier Fastai
-emoji: 🌖
-colorFrom: indigo
-colorTo: pink
+title: Simple_classifier_fastai
+app_file: simple_calssifier.py
 sdk: gradio
 sdk_version: 4.10.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

export.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:11537622d45c64cc6acbaee06a03ab1f03b2af3050da523b489e3ace08cb7bce
+size 103057580

requirements.txt

@@ -0,0 +1 @@
+fastai

simple_calssifier.py

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+from fastai.vision.all import *
+import gradio as gr
+learn = load_learner('export.pkl')
+labels = learn.dls.vocab
+
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+
+#gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)
+gr.Interface(fn=predict, inputs=gr.Image(), outputs=gr.Label(num_top_classes=3)).launch(share=True)

simple_classifier_gradio.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "device = torch.device('cpu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.11/site-packages/fastai/data/transforms.py:225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if is_categorical_dtype(col):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Could not do one pass in your dataloader, there is something wrong in it. Please see the stack trace below:\n"
+     ]
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "The MPS backend is supported on MacOS 12.3+.Current OS version can be queried using `sw_vers`",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastai\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvision\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mall\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m      4\u001b[0m path \u001b[38;5;241m=\u001b[39m untar_data(URLs\u001b[38;5;241m.\u001b[39mPETS)\n\u001b[0;32m----> 5\u001b[0m dls \u001b[38;5;241m=\u001b[39m \u001b[43mImageDataLoaders\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_name_re\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mget_image_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mimages\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpat\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m(.+)_\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43md+.jpg\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mitem_tfms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mResize\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m460\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_tfms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maug_transforms\u001b[49m\u001b[43m(\u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m224\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmin_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.75\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m learn \u001b[38;5;241m=\u001b[39m vision_learner(dls, models\u001b[38;5;241m.\u001b[39mresnet50, metrics\u001b[38;5;241m=\u001b[39maccuracy)\n\u001b[1;32m      7\u001b[0m learn\u001b[38;5;241m.\u001b[39mfine_tune(\u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m/usr/local/lib/python3.11/site-packages/fastai/vision/data.py:160\u001b[0m, in \u001b[0;36mImageDataLoaders.from_name_re\u001b[0;34m(cls, path, fnames, pat, **kwargs)\u001b[0m\n\u001b[1;32m    156\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m    157\u001b[0m \u001b[38;5;129m@delegates\u001b[39m(DataLoaders\u001b[38;5;241m.\u001b[39mfrom_dblock)\n\u001b[1;32m    158\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_name_re\u001b[39m(\u001b[38;5;28mcls\u001b[39m, path, fnames, pat, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    159\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreate from the name attrs of `fnames` in `path`s with re expression `pat`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 160\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_name_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfnames\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mRegexLabeller\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpat\u001b[49m\u001b[43m)\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/usr/local/lib/python3.11/site-packages/fastai/vision/data.py:149\u001b[0m, in \u001b[0;36mImageDataLoaders.from_name_func\u001b[0;34m(cls, path, fnames, label_func, **kwargs)\u001b[0m\n\u001b[1;32m    147\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel_func couldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt be lambda function on Windows\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    148\u001b[0m f \u001b[38;5;241m=\u001b[39m using_attr(label_func, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_path_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfnames\u001b[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[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/lib/python3.11/site-packages/fastai/vision/data.py:135\u001b[0m, in \u001b[0;36mImageDataLoaders.from_path_func\u001b[0;34m(cls, path, fnames, label_func, valid_pct, seed, item_tfms, batch_tfms, img_cls, **kwargs)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreate from list of `fnames` in `path`s with `label_func`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    130\u001b[0m dblock \u001b[38;5;241m=\u001b[39m DataBlock(blocks\u001b[38;5;241m=\u001b[39m(ImageBlock(img_cls), CategoryBlock),\n\u001b[1;32m    131\u001b[0m                    splitter\u001b[38;5;241m=\u001b[39mRandomSplitter(valid_pct, seed\u001b[38;5;241m=\u001b[39mseed),\n\u001b[1;32m    132\u001b[0m                    get_y\u001b[38;5;241m=\u001b[39mlabel_func,\n\u001b[1;32m    133\u001b[0m                    item_tfms\u001b[38;5;241m=\u001b[39mitem_tfms,\n\u001b[1;32m    134\u001b[0m                    batch_tfms\u001b[38;5;241m=\u001b[39mbatch_tfms)\n\u001b[0;32m--> 135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_dblock\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfnames\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\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/usr/local/lib/python3.11/site-packages/fastai/data/core.py:284\u001b[0m, in \u001b[0;36mDataLoaders.from_dblock\u001b[0;34m(cls, dblock, source, path, bs, val_bs, shuffle, device, **kwargs)\u001b[0m\n\u001b[1;32m    273\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m    274\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_dblock\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \n\u001b[1;32m    275\u001b[0m     dblock, \u001b[38;5;66;03m# `DataBlock` object\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    282\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    283\u001b[0m ):\n\u001b[0;32m--> 284\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdblock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataloaders\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_bs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mval_bs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshuffle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\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/usr/local/lib/python3.11/site-packages/fastai/data/block.py:157\u001b[0m, in \u001b[0;36mDataBlock.dataloaders\u001b[0;34m(self, source, path, verbose, **kwargs)\u001b[0m\n\u001b[1;32m    155\u001b[0m dsets \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatasets(source, verbose\u001b[38;5;241m=\u001b[39mverbose)\n\u001b[1;32m    156\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdls_kwargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mverbose\u001b[39m\u001b[38;5;124m'\u001b[39m: verbose}\n\u001b[0;32m--> 157\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdsets\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataloaders\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mafter_item\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem_tfms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mafter_batch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_tfms\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/usr/local/lib/python3.11/site-packages/fastai/data/core.py:337\u001b[0m, in \u001b[0;36mFilteredBase.dataloaders\u001b[0;34m(self, bs, shuffle_train, shuffle, val_shuffle, n, path, dl_type, dl_kwargs, device, drop_last, val_bs, **kwargs)\u001b[0m\n\u001b[1;32m    335\u001b[0m dl \u001b[38;5;241m=\u001b[39m dl_type(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubset(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmerge(kwargs,def_kwargs, dl_kwargs[\u001b[38;5;241m0\u001b[39m]))\n\u001b[1;32m    336\u001b[0m def_kwargs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbs\u001b[39m\u001b[38;5;124m'\u001b[39m:bs \u001b[38;5;28;01mif\u001b[39;00m val_bs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m val_bs,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mshuffle\u001b[39m\u001b[38;5;124m'\u001b[39m:val_shuffle,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;28;01mNone\u001b[39;00m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdrop_last\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;28;01mFalse\u001b[39;00m}\n\u001b[0;32m--> 337\u001b[0m dls \u001b[38;5;241m=\u001b[39m [dl] \u001b[38;5;241m+\u001b[39m \u001b[43m[\u001b[49m\u001b[43mdl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnew\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m)\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[43mmerge\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdef_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mval_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdl_kwargs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    338\u001b[0m \u001b[43m              \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_subsets\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dbunch_type(\u001b[38;5;241m*\u001b[39mdls, path\u001b[38;5;241m=\u001b[39mpath, device\u001b[38;5;241m=\u001b[39mdevice)\n",
+      "File \u001b[0;32m/usr/local/lib/python3.11/site-packages/fastai/data/core.py:337\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    335\u001b[0m dl \u001b[38;5;241m=\u001b[39m dl_type(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubset(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmerge(kwargs,def_kwargs, dl_kwargs[\u001b[38;5;241m0\u001b[39m]))\n\u001b[1;32m    336\u001b[0m def_kwargs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbs\u001b[39m\u001b[38;5;124m'\u001b[39m:bs \u001b[38;5;28;01mif\u001b[39;00m val_bs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m val_bs,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mshuffle\u001b[39m\u001b[38;5;124m'\u001b[39m:val_shuffle,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;28;01mNone\u001b[39;00m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdrop_last\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;28;01mFalse\u001b[39;00m}\n\u001b[0;32m--> 337\u001b[0m dls \u001b[38;5;241m=\u001b[39m [dl] \u001b[38;5;241m+\u001b[39m [\u001b[43mdl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnew\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m)\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[43mmerge\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdef_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mval_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdl_kwargs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    338\u001b[0m               \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_subsets)]\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dbunch_type(\u001b[38;5;241m*\u001b[39mdls, path\u001b[38;5;241m=\u001b[39mpath, device\u001b[38;5;241m=\u001b[39mdevice)\n",
+      "File \u001b[0;32m/usr/local/lib/python3.11/site-packages/fastai/data/core.py:97\u001b[0m, in \u001b[0;36mTfmdDL.new\u001b[0;34m(self, dataset, cls, **kwargs)\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_n_inp\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_types\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 97\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_one_pass\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     98\u001b[0m         res\u001b[38;5;241m.\u001b[39m_n_inp,res\u001b[38;5;241m.\u001b[39m_types \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_inp,\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_types\n\u001b[1;32m     99\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: \n",
+      "File \u001b[0;32m/usr/local/lib/python3.11/site-packages/fastai/data/core.py:79\u001b[0m, in \u001b[0;36mTfmdDL._one_pass\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_one_pass\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m     78\u001b[0m     b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdo_batch([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdo_item(\u001b[38;5;28;01mNone\u001b[39;00m)])\n\u001b[0;32m---> 79\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: b \u001b[38;5;241m=\u001b[39m \u001b[43mto_device\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     80\u001b[0m     its \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mafter_batch(b)\n\u001b[1;32m     81\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_inp \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(its, (\u001b[38;5;28mlist\u001b[39m,\u001b[38;5;28mtuple\u001b[39m)) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(its)\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(its)\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n",
+      "File \u001b[0;32m/usr/local/lib/python3.11/site-packages/fastai/torch_core.py:285\u001b[0m, in \u001b[0;36mto_device\u001b[0;34m(b, device, non_blocking)\u001b[0m\n\u001b[1;32m    283\u001b[0m \u001b[38;5;66;03m#         if hasattr(o, \"to_device\"): return o.to_device(device)\u001b[39;00m\n\u001b[1;32m    284\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m o\n\u001b[0;32m--> 285\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_inner\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/lib/python3.11/site-packages/fastai/torch_core.py:222\u001b[0m, in \u001b[0;36mapply\u001b[0;34m(func, x, *args, **kwargs)\u001b[0m\n\u001b[1;32m    220\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(func, x, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    221\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mApply `func` recursively to `x`, passing on args\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 222\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_listy(x): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(x)(\u001b[43m[\u001b[49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mo\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[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mo\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m]\u001b[49m)\n\u001b[1;32m    223\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x,\u001b[38;5;28mdict\u001b[39m):  \u001b[38;5;28;01mreturn\u001b[39;00m {k: apply(func, v, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mfor\u001b[39;00m k,v \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[1;32m    224\u001b[0m     res \u001b[38;5;241m=\u001b[39m func(x, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m/usr/local/lib/python3.11/site-packages/fastai/torch_core.py:222\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    220\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(func, x, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    221\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mApply `func` recursively to `x`, passing on args\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 222\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_listy(x): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(x)([\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mo\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[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m x])\n\u001b[1;32m    223\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x,\u001b[38;5;28mdict\u001b[39m):  \u001b[38;5;28;01mreturn\u001b[39;00m {k: apply(func, v, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mfor\u001b[39;00m k,v \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[1;32m    224\u001b[0m     res \u001b[38;5;241m=\u001b[39m func(x, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m/usr/local/lib/python3.11/site-packages/fastai/torch_core.py:224\u001b[0m, in \u001b[0;36mapply\u001b[0;34m(func, x, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_listy(x): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(x)([apply(func, o, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m x])\n\u001b[1;32m    223\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x,\u001b[38;5;28mdict\u001b[39m):  \u001b[38;5;28;01mreturn\u001b[39;00m {k: apply(func, v, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mfor\u001b[39;00m k,v \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m--> 224\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\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[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    225\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res \u001b[38;5;28;01mif\u001b[39;00m x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m retain_type(res, x)\n",
+      "File \u001b[0;32m/usr/local/lib/python3.11/site-packages/fastai/torch_core.py:282\u001b[0m, in \u001b[0;36mto_device.<locals>._inner\u001b[0;34m(o)\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_inner\u001b[39m(o):\n\u001b[0;32m--> 282\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(o,Tensor): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnon_blocking\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnon_blocking\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    283\u001b[0m \u001b[38;5;66;03m#         if hasattr(o, \"to_device\"): return o.to_device(device)\u001b[39;00m\n\u001b[1;32m    284\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m o\n",
+      "File \u001b[0;32m/usr/local/lib/python3.11/site-packages/fastai/torch_core.py:382\u001b[0m, in \u001b[0;36mTensorBase.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m    380\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    381\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--> 382\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    383\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    384\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/usr/local/lib/python3.11/site-packages/torch/_tensor.py:1295\u001b[0m, in \u001b[0;36mTensor.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m   1292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m   1294\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _C\u001b[38;5;241m.\u001b[39mDisableTorchFunctionSubclass():\n\u001b[0;32m-> 1295\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   1296\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   1297\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: The MPS backend is supported on MacOS 12.3+.Current OS version can be queried using `sw_vers`"
+     ]
+    }
+   ],
+   "source": [
+    "from fastai.vision.all import *\n",
+    "\n",
+    "\n",
+    "path = untar_data(URLs.PETS)\n",
+    "dls = ImageDataLoaders.from_name_re(path, get_image_files(path/'images'), pat='(.+)_\\d+.jpg', item_tfms=Resize(460), batch_tfms=aug_transforms(size=224, min_scale=0.75), torch.device('cpu'))\n",
+    "learn = vision_learner(dls, models.resnet50, metrics=accuracy)\n",
+    "learn.fine_tune(1)\n",
+    "learn.path = Path('.')\n",
+    "learn.export()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}