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{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"id": "87d59ec1",
"metadata": {},
"outputs": [],
"source": [
"from duckduckgo_search import ddg_images\n",
"from fastcore.all import *\n",
"def search_images(term, max_images=30):\n",
" print(f\"Searching for '{term}'\")\n",
" return L(ddg_images(term, max_results=max_images)).itemgot('image')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "6ac770e8",
"metadata": {},
"outputs": [],
"source": [
"from fastdownload import download_url\n",
"#from fastai.vision.all import *"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4e36b9c9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Searching for 'donut photo'\n",
"Searching for 'donut sun photo'\n",
"Searching for 'donut shade photo'\n",
"Searching for 'scone photo'\n",
"Searching for 'scone sun photo'\n",
"Searching for 'scone shade photo'\n"
]
}
],
"source": [
"searches = 'donut','scone'\n",
"path = Path('donut_or_not')\n",
"from time import sleep\n",
"\n",
"for o in searches:\n",
" dest = (path/o)\n",
" dest.mkdir(exist_ok=True, parents=True)\n",
" download_images(dest, urls=search_images(f'{o} photo'))\n",
" sleep(10) # Pause between searches to avoid over-loading server\n",
" download_images(dest, urls=search_images(f'{o} sun photo'))\n",
" sleep(10)\n",
" download_images(dest, urls=search_images(f'{o} shade photo'))\n",
" sleep(10)\n",
" resize_images(path/o, max_size=400, dest=path/o)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d51b4218",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"failed = verify_images(get_image_files(path))\n",
"failed\n",
"failed.map(Path.unlink)\n",
"len(failed)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0cfa1c6d",
"metadata": {},
"outputs": [],
"source": [
"dls = DataBlock(\n",
" blocks=(ImageBlock, CategoryBlock), \n",
" get_items=get_image_files, \n",
" splitter=RandomSplitter(valid_pct=0.2, seed=42),\n",
" get_y=parent_label,\n",
" item_tfms=[Resize(192, method='squish')]\n",
").dataloaders(path, bs=32)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "f6828f44",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n",
" warnings.warn(\n",
"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
" warnings.warn(msg)\n",
"Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /home/yigewu/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
]
},
{
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"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0.00/44.7M [00:00<?, ?B/s]"
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},
"metadata": {},
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{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
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},
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>error_rate</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.105229</td>\n",
" <td>0.340821</td>\n",
" <td>0.178571</td>\n",
" <td>00:23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
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{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>error_rate</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.291610</td>\n",
" <td>0.079422</td>\n",
" <td>0.035714</td>\n",
" <td>00:31</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.199587</td>\n",
" <td>0.040430</td>\n",
" <td>0.017857</td>\n",
" <td>00:34</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.141043</td>\n",
" <td>0.037486</td>\n",
" <td>0.000000</td>\n",
" <td>00:32</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learn = vision_learner(dls, resnet18, metrics=error_rate)\n",
"learn.fine_tune(3)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b80d9503",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "16920438763447d7babcf088ec67ce8e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Dropdown(options=('donut', 'scone'), value='donut'), Dropdown(options=('Train', 'Valid'), value…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from fastai.vision.widgets import *\n",
"cleaner = ImageClassifierCleaner(learn)\n",
"cleaner"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "f4f497c3",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>error_rate</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "FileNotFoundError",
"evalue": "Caught FileNotFoundError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 302, in _worker_loop\n data = fetcher.fetch(index)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 39, in fetch\n data = next(self.dataset_iter)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py\", line 140, in create_batches\n yield from map(self.do_batch, self.chunkify(res))\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/basics.py\", line 230, in chunked\n res = list(itertools.islice(it, chunk_sz))\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py\", line 155, in do_item\n try: return self.after_item(self.create_item(s))\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py\", line 162, in create_item\n if self.indexed: return self.dataset[s or 0]\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 455, in __getitem__\n res = tuple([tl[it] for tl in self.tls])\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 455, in <listcomp>\n res = tuple([tl[it] for tl in self.tls])\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 414, in __getitem__\n return self._after_item(res) if is_indexer(idx) else res.map(self._after_item)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 374, in _after_item\n def _after_item(self, o): return self.tfms(o)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 208, in __call__\n def __call__(self, o): return compose_tfms(o, tfms=self.fs, split_idx=self.split_idx)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 158, in compose_tfms\n x = f(x, **kwargs)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 81, in __call__\n def __call__(self, x, **kwargs): return self._call('encodes', x, **kwargs)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 91, in _call\n return self._do_call(getattr(self, fn), x, **kwargs)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 97, in _do_call\n return retain_type(f(x, **kwargs), x, ret)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/dispatch.py\", line 120, in __call__\n return f(*args, **kwargs)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/vision/core.py\", line 123, in create\n return cls(load_image(fn, **merge(cls._open_args, kwargs)))\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/vision/core.py\", line 98, in load_image\n im = Image.open(fn)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/PIL/Image.py\", line 3092, in open\n fp = builtins.open(filename, \"rb\")\nFileNotFoundError: [Errno 2] No such file or directory: '/diskmnt/Projects/Users/yigewu/Deep_Learning2022/pet_test/donut_or_not/scone/24f75cc7-6e8a-44e3-80bc-8c5132d2ec30.jpg'\n",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn [15], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m cleaner\u001b[38;5;241m.\u001b[39mdelete(): cleaner\u001b[38;5;241m.\u001b[39mfns[idx]\u001b[38;5;241m.\u001b[39munlink()\n\u001b[0;32m----> 2\u001b[0m \u001b[43mlearn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfine_tune\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/callback/schedule.py:165\u001b[0m, in \u001b[0;36mfine_tune\u001b[0;34m(self, epochs, base_lr, freeze_epochs, lr_mult, pct_start, div, **kwargs)\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFine tune with `Learner.freeze` for `freeze_epochs`, then with `Learner.unfreeze` for `epochs`, using discriminative LR.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfreeze()\n\u001b[0;32m--> 165\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_one_cycle\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfreeze_epochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mslice\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbase_lr\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpct_start\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.99\u001b[39;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 166\u001b[0m base_lr \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munfreeze()\n",
"File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/callback/schedule.py:119\u001b[0m, in \u001b[0;36mfit_one_cycle\u001b[0;34m(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt, start_epoch)\u001b[0m\n\u001b[1;32m 116\u001b[0m lr_max \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([h[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlr\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m h \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mopt\u001b[38;5;241m.\u001b[39mhypers])\n\u001b[1;32m 117\u001b[0m scheds \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlr\u001b[39m\u001b[38;5;124m'\u001b[39m: combined_cos(pct_start, lr_max\u001b[38;5;241m/\u001b[39mdiv, lr_max, lr_max\u001b[38;5;241m/\u001b[39mdiv_final),\n\u001b[1;32m 118\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmom\u001b[39m\u001b[38;5;124m'\u001b[39m: combined_cos(pct_start, \u001b[38;5;241m*\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmoms \u001b[38;5;28;01mif\u001b[39;00m moms \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m moms))}\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_epoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcbs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mParamScheduler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscheds\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mL\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcbs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset_opt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset_opt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_epoch\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/learner.py:256\u001b[0m, in \u001b[0;36mLearner.fit\u001b[0;34m(self, n_epoch, lr, wd, cbs, reset_opt, start_epoch)\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mopt\u001b[38;5;241m.\u001b[39mset_hypers(lr\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlr \u001b[38;5;28;01mif\u001b[39;00m lr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m lr)\n\u001b[1;32m 255\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_epoch \u001b[38;5;241m=\u001b[39m n_epoch\n\u001b[0;32m--> 256\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_fit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfit\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mCancelFitException\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[43m_end_cleanup\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/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/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/learner.py:245\u001b[0m, in \u001b[0;36mLearner._do_fit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_epoch):\n\u001b[1;32m 244\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mepoch\u001b[38;5;241m=\u001b[39mepoch\n\u001b[0;32m--> 245\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_epoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mepoch\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mCancelEpochException\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/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/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/learner.py:240\u001b[0m, in \u001b[0;36mLearner._do_epoch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_do_epoch\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 239\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_do_epoch_train()\n\u001b[0;32m--> 240\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[43m)\u001b[49m\n",
"File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/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/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/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/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/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;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mone_batch(\u001b[38;5;241m*\u001b[39mo)\n",
"File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py:129\u001b[0m, in \u001b[0;36mDataLoader.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbefore_iter()\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__idxs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_idxs() \u001b[38;5;66;03m# called in context of main process (not workers/subprocesses)\u001b[39;00m\n\u001b[0;32m--> 129\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m _loaders[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfake_l\u001b[38;5;241m.\u001b[39mnum_workers\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m0\u001b[39m](\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfake_l):\n\u001b[1;32m 130\u001b[0m \u001b[38;5;66;03m# pin_memory causes tuples to be converted to lists, so convert them back to tuples\u001b[39;00m\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpin_memory \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(b) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlist\u001b[39m: b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(b)\n\u001b[1;32m 132\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 to_device(b, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n",
"File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/dataloader.py:681\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 678\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 679\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m 680\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 681\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 682\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 683\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 684\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 685\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
"File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1376\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1374\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1375\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_task_info[idx]\n\u001b[0;32m-> 1376\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[43m_process_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1402\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._process_data\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 1400\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_try_put_index()\n\u001b[1;32m 1401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ExceptionWrapper):\n\u001b[0;32m-> 1402\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1403\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n",
"File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/_utils.py:461\u001b[0m, in \u001b[0;36mExceptionWrapper.reraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 458\u001b[0m \u001b[38;5;66;03m# If the exception takes multiple arguments, don't try to\u001b[39;00m\n\u001b[1;32m 459\u001b[0m \u001b[38;5;66;03m# instantiate since we don't know how to\u001b[39;00m\n\u001b[1;32m 460\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[0;32m--> 461\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: Caught FileNotFoundError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 302, in _worker_loop\n data = fetcher.fetch(index)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 39, in fetch\n data = next(self.dataset_iter)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py\", line 140, in create_batches\n yield from map(self.do_batch, self.chunkify(res))\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/basics.py\", line 230, in chunked\n res = list(itertools.islice(it, chunk_sz))\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py\", line 155, in do_item\n try: return self.after_item(self.create_item(s))\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py\", line 162, in create_item\n if self.indexed: return self.dataset[s or 0]\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 455, in __getitem__\n res = tuple([tl[it] for tl in self.tls])\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 455, in <listcomp>\n res = tuple([tl[it] for tl in self.tls])\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 414, in __getitem__\n return self._after_item(res) if is_indexer(idx) else res.map(self._after_item)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 374, in _after_item\n def _after_item(self, o): return self.tfms(o)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 208, in __call__\n def __call__(self, o): return compose_tfms(o, tfms=self.fs, split_idx=self.split_idx)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 158, in compose_tfms\n x = f(x, **kwargs)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 81, in __call__\n def __call__(self, x, **kwargs): return self._call('encodes', x, **kwargs)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 91, in _call\n return self._do_call(getattr(self, fn), x, **kwargs)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 97, in _do_call\n return retain_type(f(x, **kwargs), x, ret)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/dispatch.py\", line 120, in __call__\n return f(*args, **kwargs)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/vision/core.py\", line 123, in create\n return cls(load_image(fn, **merge(cls._open_args, kwargs)))\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/vision/core.py\", line 98, in load_image\n im = Image.open(fn)\n File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/PIL/Image.py\", line 3092, in open\n fp = builtins.open(filename, \"rb\")\nFileNotFoundError: [Errno 2] No such file or directory: '/diskmnt/Projects/Users/yigewu/Deep_Learning2022/pet_test/donut_or_not/scone/24f75cc7-6e8a-44e3-80bc-8c5132d2ec30.jpg'\n"
]
}
],
"source": [
"for idx in cleaner.delete(): cleaner.fns[idx].unlink()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "6cf656c1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is a: donut.\n",
"Probability it's a donut: 0.9999\n"
]
}
],
"source": [
"is_donut,_,probs = learn.predict(PILImage.create('donut.jpg'))\n",
"print(f\"This is a: {is_donut}.\")\n",
"print(f\"Probability it's a donut: {probs[0]:.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "c6e7eb32",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is a: scone.\n",
"Probability it's a donut: 0.0000\n"
]
}
],
"source": [
"is_donut,_,probs = learn.predict(PILImage.create('scone.jpg'))\n",
"print(f\"This is a: {is_donut}.\")\n",
"print(f\"Probability it's a donut: {probs[0]:.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f6e944f8",
"metadata": {},
"outputs": [],
"source": [
"learn.export('donut_model2.pkl')"
]
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|