![\"Mix](\"imgs/mix_match.png\")
\n",
"\n",
"As usual, we'll begin with end-to-end examples, then switch to the details of each of those parts."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Examples of use"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's begin with our traditional MNIST example."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from fastai.vision import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[PosixPath('/home/jupyter/.fastai/data/mnist_tiny/models'),\n",
" PosixPath('/home/jupyter/.fastai/data/mnist_tiny/valid'),\n",
" PosixPath('/home/jupyter/.fastai/data/mnist_tiny/test'),\n",
" PosixPath('/home/jupyter/.fastai/data/mnist_tiny/labels.csv'),\n",
" PosixPath('/home/jupyter/.fastai/data/mnist_tiny/train')]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path = untar_data(URLs.MNIST_TINY)\n",
"tfms = get_transforms(do_flip=False)\n",
"path.ls()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[PosixPath('/home/jupyter/.fastai/data/mnist_tiny/train/7'),\n",
" PosixPath('/home/jupyter/.fastai/data/mnist_tiny/train/3')]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(path/'train').ls()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In [`vision.data`](/vision.data.html#vision.data), we can create a [`DataBunch`](/basic_data.html#DataBunch) suitable for image classification by simply typing:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = ImageDataBunch.from_folder(path, ds_tfms=tfms, size=64)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a shortcut method which is aimed at data that is in folders following an ImageNet style, with the [`train`](/train.html#train) and `valid` directories, each containing one subdirectory per class, where all the labelled pictures are. There is also a `test` directory containing unlabelled pictures. \n",
"\n",
"Here is the same code, but this time using the data block API, which can work with any style of a dataset. All the stages, which will be explained below, can be grouped together like this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = (ImageList.from_folder(path) #Where to find the data? -> in path and its subfolders\n",
" .split_by_folder() #How to split in train/valid? -> use the folders\n",
" .label_from_folder() #How to label? -> depending on the folder of the filenames\n",
" .add_test_folder() #Optionally add a test set (here default name is test)\n",
" .transform(tfms, size=64) #Data augmentation? -> use tfms with a size of 64\n",
" .databunch()) #Finally? -> use the defaults for conversion to ImageDataBunch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can look at the created DataBunch:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"| \n", " | image_name | \n", "tags | \n", "
|---|---|---|
| 0 | \n", "train_31112 | \n", "clear primary | \n", "
| 1 | \n", "train_4300 | \n", "partly_cloudy primary water | \n", "
| 2 | \n", "train_39539 | \n", "clear primary water | \n", "
| 3 | \n", "train_12498 | \n", "agriculture clear primary road | \n", "
| 4 | \n", "train_9320 | \n", "clear primary | \n", "
| \n", " | image_name | \n", "tags | \n", "
|---|---|---|
| 0 | \n", "train_31112 | \n", "clear primary | \n", "
| 1 | \n", "train_4300 | \n", "partly_cloudy primary water | \n", "
| 2 | \n", "train_39539 | \n", "clear primary water | \n", "
| 3 | \n", "train_12498 | \n", "agriculture clear primary road | \n", "
| 4 | \n", "train_9320 | \n", "clear primary | \n", "
| idx | \n", "text | \n", "
|---|---|
| 0 | \n", "! ! ! xxmaj finally this was directed by the guy who did xxmaj big xxmaj xxunk ? xxmaj must be a replay of xxmaj jonestown - hollywood style . xxmaj xxunk ! xxbos xxmaj this is a extremely well - made film . xxmaj the acting , script and camera - work are all first - rate . xxmaj the music is good , too , though it is | \n", "
| 1 | \n", ", co - billed with xxup the xxup xxunk xxup vampire . a xxmaj spanish - xxmaj italian co - production where a series of women in a village are being murdered around the same time a local count named xxmaj yanos xxmaj xxunk is seen on xxunk , riding off with his ' man - eating ' dog behind him . \\n \\n xxmaj the xxunk already suspect | \n", "
| 2 | \n", "sad relic that is well worth seeing . xxbos i caught this on the dish last night . i liked the movie . i xxunk to xxmaj russia 3 different times ( xxunk our 2 kids ) . i ca n't put my finger on exactly why i liked this movie other than seeing \" bad \" turn \" good \" and \" good \" turn \" semi - bad | \n", "
| 3 | \n", "pushed him along . xxmaj the story ( if it can be called that ) is so full of holes it 's almost funny , xxmaj it never really explains why the hell he survived in the first place , or needs human flesh in order to survive . xxmaj the script is poorly written and the dialogue xxunk on just plane stupid . xxmaj the climax to movie ( | \n", "
| 4 | \n", "the xxunk of the xxmaj xxunk xxmaj race and had the xxunk of some of those racist xxunk . xxmaj fortunately , nothing happened like the incident in the movie where the young xxmaj caucasian man went off and started shooting at a xxunk gathering . \\n \\n i can only hope and pray that nothing like that ever will happen . \\n \\n xxmaj so is \" | \n", "
| text | \n", "target | \n", "
|---|---|
| xxbos xxmaj raising xxmaj victor xxmaj vargas : a xxmaj review \\n \\n xxmaj you know , xxmaj raising xxmaj victor xxmaj vargas is like sticking your hands into a big , xxunk bowl of xxunk . xxmaj it 's warm and gooey , but you 're not sure if it feels right . xxmaj try as i might , no matter how warm and gooey xxmaj raising xxmaj | \n", "negative | \n", "
| xxbos xxup the xxup shop xxup around xxup the xxup corner is one of the xxunk and most feel - good romantic comedies ever made . xxmaj there 's just no getting around that , and it 's hard to actually put one 's feeling for this film into words . xxmaj it 's not one of those films that tries too hard , nor does it come up with | \n", "positive | \n", "
| xxbos xxmaj now that xxmaj che(2008 ) has finished its relatively short xxmaj australian cinema run ( extremely limited xxunk screen in xxmaj xxunk , after xxunk ) , i can xxunk join both xxunk of \" xxmaj at xxmaj the xxmaj movies \" in taking xxmaj steven xxmaj soderbergh to task . \\n \\n xxmaj it 's usually satisfying to watch a film director change his style / | \n", "negative | \n", "
| xxbos xxmaj this film sat on my xxmaj xxunk for weeks before i watched it . i xxunk a self - indulgent xxunk flick about relationships gone bad . i was wrong ; this was an xxunk xxunk into the screwed - up xxunk of xxmaj new xxmaj xxunk . \\n \\n xxmaj the format is the same as xxmaj max xxmaj xxunk ' \" xxmaj la xxmaj xxunk | \n", "positive | \n", "
| xxbos xxmaj many neglect that this is n't just a classic due to the fact that it 's the first xxup 3d game , or even the first xxunk - up . xxmaj it 's also one of the first xxunk games , one of the xxunk definitely the first ) truly claustrophobic games , and just a pretty well - xxunk gaming experience in general . xxmaj with graphics | \n", "positive | \n", "
| workclass | \n", "education | \n", "marital-status | \n", "occupation | \n", "relationship | \n", "race | \n", "sex | \n", "native-country | \n", "education-num_na | \n", "education-num | \n", "hours-per-week | \n", "age | \n", "capital-loss | \n", "fnlwgt | \n", "capital-gain | \n", "target | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Private | \n", "Assoc-acdm | \n", "Married-civ-spouse | \n", "Tech-support | \n", "Husband | \n", "White | \n", "Male | \n", "United-States | \n", "False | \n", "0.7511 | \n", "-2.4656 | \n", "-0.3362 | \n", "4.8553 | \n", "-0.9396 | \n", "-0.1459 | \n", "<50k | \n", "
| Private | \n", "HS-grad | \n", "Divorced | \n", "Other-service | \n", "Not-in-family | \n", "White | \n", "Female | \n", "United-States | \n", "False | \n", "-0.4224 | \n", "-0.0356 | \n", "0.7632 | \n", "-0.2164 | \n", "-0.0449 | \n", "-0.1459 | \n", "<50k | \n", "
| Private | \n", "Some-college | \n", "Married-civ-spouse | \n", "Exec-managerial | \n", "Husband | \n", "White | \n", "Male | \n", "United-States | \n", "False | \n", "-0.0312 | \n", "-0.0356 | \n", "0.9098 | \n", "-0.2164 | \n", "0.6116 | \n", "-0.1459 | \n", ">=50k | \n", "
| Private | \n", "9th | \n", "Divorced | \n", "Transport-moving | \n", "Not-in-family | \n", "White | \n", "Male | \n", "United-States | \n", "False | \n", "-1.9869 | \n", "-0.0356 | \n", "-0.5561 | \n", "-0.2164 | \n", "-0.5796 | \n", "-0.1459 | \n", "<50k | \n", "
| Private | \n", "Masters | \n", "Married-civ-spouse | \n", "Prof-specialty | \n", "Husband | \n", "White | \n", "Male | \n", "United-States | \n", "False | \n", "1.5334 | \n", "0.7743 | \n", "-0.5561 | \n", "-0.2164 | \n", "-0.0140 | \n", "-0.1459 | \n", "<50k | \n", "
class ItemList[source][test]ItemList(**`items`**:`Iterator`\\[`T_co`\\], **`path`**:`PathOrStr`=***`'.'`***, **`label_cls`**:`Callable`=***`None`***, **`inner_df`**:`Any`=***`None`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **`x`**:`ItemList`=***`None`***, **`ignore_empty`**:`bool`=***`False`***)\n",
"\n",
"Tests found for ItemList:
Some other tests where ItemList is used:
pytest -sv tests/test_data_block.py::test_category [source]pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]pytest -sv tests/test_data_block.py::test_multi_category [source]pytest -sv tests/test_data_block.py::test_regression [source]pytest -sv tests/test_data_block.py::test_split_subsets [source]pytest -sv tests/test_data_block.py::test_splitdata_datasets [source]To run tests please refer to this guide.
from_folder[source][test]from_folder(**`path`**:`PathOrStr`, **`extensions`**:`StrList`=***`None`***, **`recurse`**:`bool`=***`True`***, **`include`**:`OptStrList`=***`None`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n",
"\n",
"from_df[source][test]from_df(**`df`**:`DataFrame`, **`path`**:`PathOrStr`=***`'.'`***, **`cols`**:`IntsOrStrs`=***`0`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n",
"\n",
"Tests found for from_df:
Some other tests where from_df is used:
pytest -sv tests/test_data_block.py::test_category [source]pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]pytest -sv tests/test_data_block.py::test_multi_category [source]pytest -sv tests/test_data_block.py::test_regression [source]To run tests please refer to this guide.
| \n", " | name | \n", "label | \n", "
|---|---|---|
| 0 | \n", "train/3/7463.png | \n", "0 | \n", "
| 1 | \n", "train/3/21102.png | \n", "0 | \n", "
| 2 | \n", "train/3/31559.png | \n", "0 | \n", "
| 3 | \n", "train/3/46882.png | \n", "0 | \n", "
| 4 | \n", "train/3/26209.png | \n", "0 | \n", "
from_csv[source][test]from_csv(**`path`**:`PathOrStr`, **`csv_name`**:`str`, **`cols`**:`IntsOrStrs`=***`0`***, **`delimiter`**:`str`=***`None`***, **`header`**:`str`=***`'infer'`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n",
"\n",
"No tests found for from_csv. To contribute a test please refer to this guide and this discussion.
filter_by_func[source][test]filter_by_func(**`func`**:`Callable`) → `ItemList`\n",
"\n",
"No tests found for filter_by_func. To contribute a test please refer to this guide and this discussion.
| \n", " | name | \n", "label | \n", "
|---|---|---|
| 0 | \n", "train/3/7463.png | \n", "0 | \n", "
| 1 | \n", "train/3/21102.png | \n", "0 | \n", "
| 2 | \n", "train/3/31559.png | \n", "0 | \n", "
| 3 | \n", "train/3/46882.png | \n", "0 | \n", "
| 4 | \n", "train/3/26209.png | \n", "0 | \n", "
filter_by_folder[source][test]filter_by_folder(**`include`**=***`None`***, **`exclude`**=***`None`***)\n",
"\n",
"No tests found for filter_by_folder. To contribute a test please refer to this guide and this discussion.
filter_by_rand[source][test]filter_by_rand(**`p`**:`float`, **`seed`**:`int`=***`None`***)\n",
"\n",
"No tests found for filter_by_rand. To contribute a test please refer to this guide and this discussion.
to_text[source][test]to_text(**`fn`**:`str`)\n",
"\n",
"No tests found for to_text. To contribute a test please refer to this guide and this discussion.
| \n", " | name | \n", "label | \n", "
|---|---|---|
| 0 | \n", "train/3/7463.png | \n", "0 | \n", "
| 1 | \n", "train/3/21102.png | \n", "0 | \n", "
| 2 | \n", "train/3/31559.png | \n", "0 | \n", "
| 3 | \n", "train/3/46882.png | \n", "0 | \n", "
| 4 | \n", "train/3/26209.png | \n", "0 | \n", "
use_partial_data[source][test]use_partial_data(**`sample_pct`**:`float`=***`0.01`***, **`seed`**:`int`=***`None`***) → `ItemList`\n",
"\n",
"No tests found for use_partial_data. To contribute a test please refer to this guide and this discussion.
analyze_pred[source][test]analyze_pred(**`pred`**:`Tensor`)\n",
"\n",
"No tests found for analyze_pred. To contribute a test please refer to this guide and this discussion.
get[source][test]get(**`i`**) → `Any`\n",
"\n",
"No tests found for get. To contribute a test please refer to this guide and this discussion.
new[source][test]new(**`items`**:`Iterator`\\[`T_co`\\], **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n",
"\n",
"No tests found for new. To contribute a test please refer to this guide and this discussion.
reconstruct[source][test]reconstruct(**`t`**:`Tensor`, **`x`**:`Tensor`=***`None`***)\n",
"\n",
"No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.
split_none[source][test]split_none()\n",
"\n",
"No tests found for split_none. To contribute a test please refer to this guide and this discussion.
split_by_rand_pct[source][test]split_by_rand_pct(**`valid_pct`**:`float`=***`0.2`***, **`seed`**:`int`=***`None`***) → `ItemLists`\n",
"\n",
"split_subsets[source][test]split_subsets(**`train_size`**:`float`, **`valid_size`**:`float`, **`seed`**=***`None`***) → `ItemLists`\n",
"\n",
"split_by_files[source][test]split_by_files(**`valid_names`**:`ItemList`) → `ItemLists`\n",
"\n",
"No tests found for split_by_files. To contribute a test please refer to this guide and this discussion.
split_by_fname_file[source][test]split_by_fname_file(**`fname`**:`PathOrStr`, **`path`**:`PathOrStr`=***`None`***) → `ItemLists`\n",
"\n",
"No tests found for split_by_fname_file. To contribute a test please refer to this guide and this discussion.
split_by_folder[source][test]split_by_folder(**`train`**:`str`=***`'train'`***, **`valid`**:`str`=***`'valid'`***) → `ItemLists`\n",
"\n",
"split_by_idx[source][test]split_by_idx(**`valid_idx`**:`Collection`\\[`int`\\]) → `ItemLists`\n",
"\n",
"Tests found for split_by_idx:
Some other tests where split_by_idx is used:
pytest -sv tests/test_data_block.py::test_category [source]pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]pytest -sv tests/test_data_block.py::test_multi_category [source]To run tests please refer to this guide.
split_by_idxs[source][test]split_by_idxs(**`train_idx`**, **`valid_idx`**)\n",
"\n",
"No tests found for split_by_idxs. To contribute a test please refer to this guide and this discussion.
split_by_list[source][test]split_by_list(**`train`**, **`valid`**)\n",
"\n",
"No tests found for split_by_list. To contribute a test please refer to this guide and this discussion.
split_by_valid_func[source][test]split_by_valid_func(**`func`**:`Callable`) → `ItemLists`\n",
"\n",
"No tests found for split_by_valid_func. To contribute a test please refer to this guide and this discussion.
split_from_df[source][test]split_from_df(**`col`**:`IntsOrStrs`=***`2`***)\n",
"\n",
"No tests found for split_from_df. To contribute a test please refer to this guide and this discussion.
get_label_cls[source][test]get_label_cls(**`labels`**, **`label_cls`**:`Callable`=***`None`***, **`label_delim`**:`str`=***`None`***, **\\*\\*`kwargs`**)\n",
"\n",
"No tests found for get_label_cls. To contribute a test please refer to this guide and this discussion.
| \n", " | x | \n", "y | \n", "
|---|---|---|
| 0 | \n", "train/7/8845.png | \n", "7 | \n", "
| 1 | \n", "train/7/8297.png | \n", "7 | \n", "
| 2 | \n", "train/7/7945.png | \n", "7 | \n", "
| 3 | \n", "train/7/8186.png | \n", "7 | \n", "
| 4 | \n", "train/7/9843.png | \n", "7 | \n", "
label_empty[source][test]label_empty(**\\*\\*`kwargs`**)\n",
"\n",
"No tests found for label_empty. To contribute a test please refer to this guide and this discussion.
label_from_df[source][test]label_from_df(**`cols`**:`IntsOrStrs`=***`1`***, **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**)\n",
"\n",
"Tests found for label_from_df:
Some other tests where label_from_df is used:
pytest -sv tests/test_data_block.py::test_category [source]pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]pytest -sv tests/test_data_block.py::test_multi_category [source]pytest -sv tests/test_data_block.py::test_regression [source]To run tests please refer to this guide.
label_const[source][test]label_const(**`const`**:`Any`=***`0`***, **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n",
"\n",
"label_from_folder[source][test]label_from_folder(**`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n",
"\n",
"Tests found for label_from_folder:
pytest -sv tests/test_text_data.py::test_filter_classes [source]pytest -sv tests/test_text_data.py::test_from_folder [source]Some other tests where label_from_folder is used:
pytest -sv tests/test_data_block.py::test_wrong_order [source]To run tests please refer to this guide.
label_from_func[source][test]label_from_func(**`func`**:`Callable`, **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n",
"\n",
"No tests found for label_from_func. To contribute a test please refer to this guide and this discussion.
label_from_re[source][test]label_from_re(**`pat`**:`str`, **`full_path`**:`bool`=***`False`***, **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n",
"\n",
"No tests found for label_from_re. To contribute a test please refer to this guide and this discussion.
class CategoryList[source][test]CategoryList(**`items`**:`Iterator`\\[`T_co`\\], **`classes`**:`Collection`\\[`T_co`\\]=***`None`***, **`label_delim`**:`str`=***`None`***, **\\*\\*`kwargs`**) :: [`CategoryListBase`](/data_block.html#CategoryListBase)\n",
"\n",
"No tests found for CategoryList. To contribute a test please refer to this guide and this discussion.
class MultiCategoryList[source][test]MultiCategoryList(**`items`**:`Iterator`\\[`T_co`\\], **`classes`**:`Collection`\\[`T_co`\\]=***`None`***, **`label_delim`**:`str`=***`None`***, **`one_hot`**:`bool`=***`False`***, **\\*\\*`kwargs`**) :: [`CategoryListBase`](/data_block.html#CategoryListBase)\n",
"\n",
"No tests found for MultiCategoryList. To contribute a test please refer to this guide and this discussion.
class FloatList[source][test]FloatList(**`items`**:`Iterator`\\[`T_co`\\], **`log`**:`bool`=***`False`***, **`classes`**:`Collection`\\[`T_co`\\]=***`None`***, **\\*\\*`kwargs`**) :: [`ItemList`](/data_block.html#ItemList)\n",
"\n",
"No tests found for FloatList. To contribute a test please refer to this guide and this discussion.
class EmptyLabelList[source][test]EmptyLabelList(**`items`**:`Iterator`\\[`T_co`\\], **`path`**:`PathOrStr`=***`'.'`***, **`label_cls`**:`Callable`=***`None`***, **`inner_df`**:`Any`=***`None`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **`x`**:`ItemList`=***`None`***, **`ignore_empty`**:`bool`=***`False`***) :: [`ItemList`](/data_block.html#ItemList)\n",
"\n",
"No tests found for EmptyLabelList. To contribute a test please refer to this guide and this discussion.
class PreProcessor[source][test]PreProcessor(**`ds`**:`Collection`\\[`T_co`\\]=***`None`***)\n",
"\n",
"No tests found for PreProcessor. To contribute a test please refer to this guide and this discussion.
process_one[source][test]process_one(**`item`**:`Any`)\n",
"\n",
"process[source][test]process(**`ds`**:`Collection`\\[`T_co`\\])\n",
"\n",
"No tests found for process. To contribute a test please refer to this guide and this discussion.
class CategoryProcessor[source][test]CategoryProcessor(**`ds`**:[`ItemList`](/data_block.html#ItemList)) :: [`PreProcessor`](/data_block.html#PreProcessor)\n",
"\n",
"No tests found for CategoryProcessor. To contribute a test please refer to this guide and this discussion.
generate_classes[source][test]generate_classes(**`items`**)\n",
"\n",
"No tests found for generate_classes. To contribute a test please refer to this guide and this discussion.
class MultiCategoryProcessor[source][test]MultiCategoryProcessor(**`ds`**:[`ItemList`](/data_block.html#ItemList), **`one_hot`**:`bool`=***`False`***) :: [`CategoryProcessor`](/data_block.html#CategoryProcessor)\n",
"\n",
"No tests found for MultiCategoryProcessor. To contribute a test please refer to this guide and this discussion.
generate_classes[source][test]generate_classes(**`items`**)\n",
"\n",
"No tests found for generate_classes. To contribute a test please refer to this guide and this discussion.
transform[source][test]transform(**`tfms`**:`Optional`\\[`Tuple`\\[`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]\\]\\]=***`(None, None)`***, **\\*\\*`kwargs`**)\n",
"\n",
"No tests found for transform. To contribute a test please refer to this guide and this discussion.
add_test[source][test]add_test(**`items`**:`Iterator`\\[`T_co`\\], **`label`**:`Any`=***`None`***)\n",
"\n",
"No tests found for add_test. To contribute a test please refer to this guide and this discussion.
add_test_folder[source][test]add_test_folder(**`test_folder`**:`str`=***`'test'`***, **`label`**:`Any`=***`None`***)\n",
"\n",
"No tests found for add_test_folder. To contribute a test please refer to this guide and this discussion.
databunch[source][test]databunch(**`path`**:`PathOrStr`=***`None`***, **`bs`**:`int`=***`64`***, **`val_bs`**:`int`=***`None`***, **`num_workers`**:`int`=***`4`***, **`dl_tfms`**:`Optional`\\[`Collection`\\[`Callable`\\]\\]=***`None`***, **`device`**:[`device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch-device)=***`None`***, **`collate_fn`**:`Callable`=***`'data_collate'`***, **`no_check`**:`bool`=***`False`***, **\\*\\*`kwargs`**) → `DataBunch`\n",
"\n",
"class LabelList[source][test]LabelList(**`x`**:[`ItemList`](/data_block.html#ItemList), **`y`**:[`ItemList`](/data_block.html#ItemList), **`tfms`**:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]=***`None`***, **`tfm_y`**:`bool`=***`False`***, **\\*\\*`kwargs`**) :: [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)\n",
"\n",
"No tests found for LabelList. To contribute a test please refer to this guide and this discussion.
export[source][test]export(**`fn`**:`PathOrStr`, **\\*\\*`kwargs`**)\n",
"\n",
"No tests found for export. To contribute a test please refer to this guide and this discussion.
transform_y[source][test]transform_y(**`tfms`**:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]=***`None`***, **\\*\\*`kwargs`**)\n",
"\n",
"No tests found for transform_y. To contribute a test please refer to this guide and this discussion.
get_state[source][test]get_state(**\\*\\*`kwargs`**)\n",
"\n",
"No tests found for get_state. To contribute a test please refer to this guide and this discussion.
load_empty[source][test]load_empty(**`path`**:`PathOrStr`, **`fn`**:`PathOrStr`)\n",
"\n",
"No tests found for load_empty. To contribute a test please refer to this guide and this discussion.
load_state[source][test]load_state(**`path`**:`PathOrStr`, **`state`**:`dict`) → `LabelList`\n",
"\n",
"No tests found for load_state. To contribute a test please refer to this guide and this discussion.
process[source][test]process(**`xp`**:[`PreProcessor`](/data_block.html#PreProcessor)=***`None`***, **`yp`**:[`PreProcessor`](/data_block.html#PreProcessor)=***`None`***, **`name`**:`str`=***`None`***)\n",
"\n",
"No tests found for process. To contribute a test please refer to this guide and this discussion.
set_item[source][test]set_item(**`item`**)\n",
"\n",
"No tests found for set_item. To contribute a test please refer to this guide and this discussion.
to_df[source][test]to_df()\n",
"\n",
"No tests found for to_df. To contribute a test please refer to this guide and this discussion.
to_csv[source][test]to_csv(**`dest`**:`str`)\n",
"\n",
"No tests found for to_csv. To contribute a test please refer to this guide and this discussion.
transform[source][test]transform(**`tfms`**:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], **`tfm_y`**:`bool`=***`None`***, **\\*\\*`kwargs`**)\n",
"\n",
"No tests found for transform. To contribute a test please refer to this guide and this discussion.
class ItemLists[source][test]ItemLists(**`path`**:`PathOrStr`, **`train`**:[`ItemList`](/data_block.html#ItemList), **`valid`**:[`ItemList`](/data_block.html#ItemList))\n",
"\n",
"No tests found for ItemLists. To contribute a test please refer to this guide and this discussion.
label_from_lists[source][test]label_from_lists(**`train_labels`**:`Iterator`\\[`T_co`\\], **`valid_labels`**:`Iterator`\\[`T_co`\\], **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n",
"\n",
"No tests found for label_from_lists. To contribute a test please refer to this guide and this discussion.
transform[source][test]transform(**`tfms`**:`Optional`\\[`Tuple`\\[`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]\\]\\]=***`(None, None)`***, **\\*\\*`kwargs`**)\n",
"\n",
"No tests found for transform. To contribute a test please refer to this guide and this discussion.
transform_y[source][test]transform_y(**`tfms`**:`Optional`\\[`Tuple`\\[`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]\\]\\]=***`(None, None)`***, **\\*\\*`kwargs`**)\n",
"\n",
"No tests found for transform_y. To contribute a test please refer to this guide and this discussion.
class LabelLists[source][test]LabelLists(**`path`**:`PathOrStr`, **`train`**:[`ItemList`](/data_block.html#ItemList), **`valid`**:[`ItemList`](/data_block.html#ItemList)) :: [`ItemLists`](/data_block.html#ItemLists)\n",
"\n",
"No tests found for LabelLists. To contribute a test please refer to this guide and this discussion.
get_processors[source][test]get_processors()\n",
"\n",
"No tests found for get_processors. To contribute a test please refer to this guide and this discussion.
load_empty[source][test]load_empty(**`path`**:`PathOrStr`, **`fn`**:`PathOrStr`=***`'export.pkl'`***)\n",
"\n",
"No tests found for load_empty. To contribute a test please refer to this guide and this discussion.
load_state[source][test]load_state(**`path`**:`PathOrStr`, **`state`**:`dict`)\n",
"\n",
"No tests found for load_state. To contribute a test please refer to this guide and this discussion.
process[source][test]process()\n",
"\n",
"No tests found for process. To contribute a test please refer to this guide and this discussion.
get_files[source][test]get_files(**`path`**:`PathOrStr`, **`extensions`**:`StrList`=***`None`***, **`recurse`**:`bool`=***`False`***, **`include`**:`OptStrList`=***`None`***) → `FilePathList`\n",
"\n",
"No tests found for get_files. To contribute a test please refer to this guide and this discussion.
new[source][test]new(**`items`**:`Iterator`\\[`T_co`\\], **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n",
"\n",
"No tests found for new. To contribute a test please refer to this guide and this discussion.
new[source][test]new(**`x`**, **`y`**, **\\*\\*`kwargs`**) → `LabelList`\n",
"\n",
"No tests found for new. To contribute a test please refer to this guide and this discussion.
get[source][test]get(**`i`**)\n",
"\n",
"No tests found for get. To contribute a test please refer to this guide and this discussion.
predict[source][test]predict(**`res`**)\n",
"\n",
"No tests found for predict. To contribute a test please refer to this guide and this discussion.
new[source][test]new(**`items`**:`Iterator`\\[`T_co`\\], **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n",
"\n",
"No tests found for new. To contribute a test please refer to this guide and this discussion.
process_one[source][test]process_one(**`item`**:[`ItemBase`](/core.html#ItemBase), **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***)\n",
"\n",
"process[source][test]process(**`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***)\n",
"\n",
"No tests found for process. To contribute a test please refer to this guide and this discussion.
process_one[source][test]process_one(**`item`**)\n",
"\n",
"get[source][test]get(**`i`**)\n",
"\n",
"No tests found for get. To contribute a test please refer to this guide and this discussion.
process_one[source][test]process_one(**`item`**)\n",
"\n",
"create_classes[source][test]create_classes(**`classes`**)\n",
"\n",
"No tests found for create_classes. To contribute a test please refer to this guide and this discussion.
process[source][test]process(**`ds`**)\n",
"\n",
"No tests found for process. To contribute a test please refer to this guide and this discussion.
get[source][test]get(**`i`**)\n",
"\n",
"No tests found for get. To contribute a test please refer to this guide and this discussion.
new[source][test]new(**`items`**:`Iterator`\\[`T_co`\\], **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n",
"\n",
"No tests found for new. To contribute a test please refer to this guide and this discussion.
reconstruct[source][test]reconstruct(**`t`**)\n",
"\n",
"No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.
analyze_pred[source][test]analyze_pred(**`pred`**, **`thresh`**:`float`=***`0.5`***)\n",
"\n",
"No tests found for analyze_pred. To contribute a test please refer to this guide and this discussion.
reconstruct[source][test]reconstruct(**`t`**)\n",
"\n",
"No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.
reconstruct[source][test]reconstruct(**`t`**)\n",
"\n",
"No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.
analyze_pred[source][test]analyze_pred(**`pred`**, **`thresh`**:`float`=***`0.5`***)\n",
"\n",
"No tests found for analyze_pred. To contribute a test please refer to this guide and this discussion.
reconstruct[source][test]reconstruct(**`t`**:`Tensor`, **`x`**:`Tensor`=***`None`***)\n",
"\n",
"No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.
get[source][test]get(**`i`**)\n",
"\n",
"No tests found for get. To contribute a test please refer to this guide and this discussion.
databunch[source][test]databunch(**\\*\\*`kwargs`**)\n",
"\n",
"add[source][test]add(**`items`**:`ItemList`)\n",
"\n",
"No tests found for add. To contribute a test please refer to this guide and this discussion.