USPS dataset (#5647)

* added usps dataset

* fixed type issues

* fix mobilnet norm layer test (#5643)

* xfail mobilnet norm layer test

* fix test

* More robust check in tests for 16 bits images (#5652)

* Prefer nvidia channel for conda builds (#5648)

To mitigate missing `libcupti.so` dependency

* fix torchdata CI installation (#5657)

* update urls for kinetics dataset (#5578)

* update urls for kinetics dataset

* update urls for kinetics dataset

* remove errors

* update the changes and add test option to split

* added test to valid values for split arg

* change .txt to .csv for annotation url of k600

Co-authored-by: Nicolas Hug <[email protected]>

* Port Multi-weight support from prototype to main (#5618)

* Moving basefiles outside of prototype and porting Alexnet, ConvNext, Densenet and EfficientNet.

* Porting googlenet

* Porting inception

* Porting mnasnet

* Porting mobilenetv2

* Porting mobilenetv3

* Porting regnet

* Porting resnet

* Porting shufflenetv2

* Porting squeezenet

* Porting vgg

* Porting vit

* Fix docstrings

* Fixing imports

* Adding missing import

* Fix mobilenet imports

* Fix tests

* Fix prototype tests

* Exclude get_weight from models on test

* Fix init files

* Porting googlenet

* Porting inception

* porting mobilenetv2

* porting mobilenetv3

* porting resnet

* porting shufflenetv2

* Fix test and linter

* Fixing docs.

* Porting Detection models (#5617)

* fix inits

* fix docs

* Port faster_rcnn

* Port fcos

* Port keypoint_rcnn

* Port mask_rcnn

* Port retinanet

* Port ssd

* Port ssdlite

* Fix linter

* Fixing tests

* Fixing tests

* Fixing vgg test

* Porting Optical Flow, Segmentation, Video models (#5619)

* Porting raft

* Porting video resnet

* Porting deeplabv3

* Porting fcn and lraspp

* Fixing the tests and linter

* Porting docs, examples, tutorials and galleries (#5620)

* Fix examples, tutorials and gallery

* Update gallery/plot_optical_flow.py

Co-authored-by: Nicolas Hug <[email protected]>

* Fix import

* Revert hardcoded normalization

* fix uncommitted changes

* Fix bug

* Fix more bugs

* Making resize optional for segmentation

* Fixing preset

* Fix mypy

* Fixing documentation strings

* Fix flake8

* minor refactoring

Co-authored-by: Nicolas Hug <[email protected]>

* Resolve conflict

* Porting model tests (#5622)

* Porting tests

* Remove unnecessary variable

* Fix linter

* Move prototype to extended tests

* Fix download models job

* Update CI on Multiweight branch to use the new weight download approach (#5628)

* port Pad to prototype transforms (#5621)

* port Pad to prototype transforms

* use literal

* Bump up LibTorchvision version number for Podspec to release Cocoapods (#5624)

Co-authored-by: Anton Thomma <[email protected]>
Co-authored-by: Vasilis Vryniotis <[email protected]>

* pre-download model weights in CI docs build (#5625)

* pre-download model weights in CI docs build

* move changes into template

* change docs image

* Regenerated config.yml

Co-authored-by: Philip Meier <[email protected]>
Co-authored-by: Anton Thomma <[email protected]>
Co-authored-by: Anton Thomma <[email protected]>

* Porting reference scripts and updating presets (#5629)

* Making _preset.py classes

* Remove support of targets on presets.

* Rewriting the video preset

* Adding tests to check that the bundled transforms are JIT scriptable

* Rename all presets from *Eval to *Inference

* Minor refactoring

* Remove --prototype and --pretrained from reference scripts

* remove  pretained_backbone refs

* Corrections and simplifications

* Fixing bug

* Fixing linter

* Fix flake8

* restore documentation example

* minor fixes

* fix optical flow missing param

* Fixing commands

* Adding weights_backbone support in detection and segmentation

* Updating the commands for InceptionV3

* Setting `weights_backbone` to its fully BC value (#5653)

* Replace default `weights_backbone=None` with its BC values.

* Fixing tests

* Fix linter

* Update docs.

* Update preprocessing on reference scripts.

* Change qat/ptq to their full values.

* Refactoring preprocessing

* Fix video preset

* No initialization on VGG if pretrained

* Fix warning messages for backbone utils.

* Adding star to all preset constructors.

* Fix mypy.

Co-authored-by: Nicolas Hug <[email protected]>
Co-authored-by: Philip Meier <[email protected]>
Co-authored-by: Anton Thomma <[email protected]>
Co-authored-by: Anton Thomma <[email protected]>

* Apply suggestions from code review

Co-authored-by: Philip Meier <[email protected]>

* use decompressor for extracting bz2

* Apply suggestions from code review

Co-authored-by: Philip Meier <[email protected]>

* Apply suggestions from code review

Co-authored-by: Philip Meier <[email protected]>

* fixed lint fails

* added tests for USPS

* check image shape

* fix tests

* check shape on image directly

* Apply suggestions from code review

Co-authored-by: Philip Meier <[email protected]>

* removed test and comments

* Update test/test_prototype_builtin_datasets.py

Co-authored-by: Nicolas Hug <[email protected]>

Co-authored-by: Philip Meier <[email protected]>
Co-authored-by: Nicolas Hug <[email protected]>
Co-authored-by: Nikita Shulga <[email protected]>
Co-authored-by: Sahil Goyal <[email protected]>
Co-authored-by: Vasilis Vryniotis <[email protected]>
Co-authored-by: Anton Thomma <[email protected]>
Co-authored-by: Anton Thomma <[email protected]>

Author: 8 people

test/builtin_dataset_mocks.py

@@ -1,3 +1,4 @@
+import bz2
 import collections.abc
 import csv
 import functools
@@ -1431,3 +1432,21 @@ def stanford_cars(info, root, config):
         make_tar(root, "car_devkit.tgz", devkit, compression="gz")
 
     return num_samples
+
+
+@register_mock
+def usps(info, root, config):
+    num_samples = {"train": 15, "test": 7}[config.split]
+
+    with bz2.open(root / f"usps{'.t' if not config.split == 'train' else ''}.bz2", "wb") as fh:
+        lines = []
+        for _ in range(num_samples):
+            label = make_tensor(1, low=1, high=11, dtype=torch.int)
+            values = make_tensor(256, low=-1, high=1, dtype=torch.float)
+            lines.append(
+                " ".join([f"{int(label)}", *(f"{idx}:{float(value):.6f}" for idx, value in enumerate(values, 1))])
+            )
+
+        fh.write("\n".join(lines).encode())
+
+    return num_samples

test/test_prototype_builtin_datasets.py

@@ -12,7 +12,7 @@
 from torchdata.datapipes.iter import IterDataPipe, Shuffler
 from torchvision._utils import sequence_to_str
 from torchvision.prototype import transforms, datasets
-
+from torchvision.prototype.features import Image, Label
 
 assert_samples_equal = functools.partial(
     assert_equal, pair_types=(TensorLikePair, ObjectPair), rtol=0, atol=0, equal_nan=True
@@ -180,3 +180,20 @@ def test_label_matches_path(self, test_home, dataset_mock, config):
         for sample in dataset:
             label_from_path = int(Path(sample["path"]).parent.name)
             assert sample["label"] == label_from_path
+
+
+@parametrize_dataset_mocks(DATASET_MOCKS["usps"])
+class TestUSPS:
+    def test_sample_content(self, test_home, dataset_mock, config):
+        dataset_mock.prepare(test_home, config)
+
+        dataset = datasets.load(dataset_mock.name, **config)
+
+        for sample in dataset:
+            assert "image" in sample
+            assert "label" in sample
+
+            assert isinstance(sample["image"], Image)
+            assert isinstance(sample["label"], Label)
+
+            assert sample["image"].shape == (1, 16, 16)

torchvision/prototype/datasets/_builtin/__init__.py

@@ -17,4 +17,5 @@
 from .semeion import SEMEION
 from .stanford_cars import StanfordCars
 from .svhn import SVHN
+from .usps import USPS
 from .voc import VOC

torchvision/prototype/datasets/_builtin/usps.py

@@ -0,0 +1,54 @@
+from typing import Any, Dict, List
+
+import torch
+from torchdata.datapipes.iter import IterDataPipe, LineReader, Mapper, Decompressor
+from torchvision.prototype.datasets.utils import Dataset, DatasetInfo, DatasetConfig, OnlineResource, HttpResource
+from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling
+from torchvision.prototype.features import Image, Label
+
+
+class USPS(Dataset):
+    def _make_info(self) -> DatasetInfo:
+        return DatasetInfo(
+            "usps",
+            homepage="https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#usps",
+            valid_options=dict(
+                split=("train", "test"),
+            ),
+            categories=10,
+        )
+
+    _URL = "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass"
+
+    _RESOURCES = {
+        "train": HttpResource(
+            f"{_URL}/usps.bz2", sha256="3771e9dd6ba685185f89867b6e249233dd74652389f263963b3b741e994b034f"
+        ),
+        "test": HttpResource(
+            f"{_URL}/usps.t.bz2", sha256="a9c0164e797d60142a50604917f0baa604f326e9a689698763793fa5d12ffc4e"
+        ),
+    }
+
+    def resources(self, config: DatasetConfig) -> List[OnlineResource]:
+        return [USPS._RESOURCES[config.split]]
+
+    def _prepare_sample(self, line: str) -> Dict[str, Any]:
+        label, *values = line.strip().split(" ")
+        values = [float(value.split(":")[1]) for value in values]
+        pixels = torch.tensor(values).add_(1).div_(2)
+        return dict(
+            image=Image(pixels.reshape(16, 16)),
+            label=Label(int(label) - 1, categories=self.categories),
+        )
+
+    def _make_datapipe(
+        self,
+        resource_dps: List[IterDataPipe],
+        *,
+        config: DatasetConfig,
+    ) -> IterDataPipe[Dict[str, Any]]:
+        dp = Decompressor(resource_dps[0])
+        dp = LineReader(dp, decode=True, return_path=False)
+        dp = hint_sharding(dp)
+        dp = hint_shuffling(dp)
+        return Mapper(dp, self._prepare_sample)