[fbsync] USPS dataset (#5647)

Summary: * 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 * 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 * 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 * 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) * 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 * 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. * Apply suggestions from code review * use decompressor for extracting bz2 * Apply suggestions from code review * Apply suggestions from code review * fixed lint fails * added tests for USPS * check image shape * fix tests * check shape on image directly * Apply suggestions from code review * removed test and comments * Update test/test_prototype_builtin_datasets.py (Note: this ignores all push blocking failures!) Reviewed By: datumbox Differential Revision: D35216783 fbshipit-source-id: 556a63a89f15d1541ac2b479244a7b6c564eff14 Co-authored-by: Nicolas Hug <contact@nicolas-hug.com> Co-authored-by: Nicolas Hug <contact@nicolas-hug.com> Co-authored-by: Nicolas Hug <contact@nicolas-hug.com> Co-authored-by: Anton Thomma <anton@pri.co.nz> Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com> Co-authored-by: Philip Meier <github.pmeier@posteo.de> Co-authored-by: Anton Thomma <11010310+thommaa@users.noreply.github.com> Co-authored-by: Anton Thomma <anton@pri.co.nz> Co-authored-by: Nicolas Hug <contact@nicolas-hug.com> Co-authored-by: Philip Meier <github.pmeier@posteo.de> Co-authored-by: Anton Thomma <11010310+thommaa@users.noreply.github.com> Co-authored-by: Anton Thomma <anton@pri.co.nz> Co-authored-by: Philip Meier <github.pmeier@posteo.de> Co-authored-by: Philip Meier <github.pmeier@posteo.de> Co-authored-by: Philip Meier <github.pmeier@posteo.de> Co-authored-by: Philip Meier <github.pmeier@posteo.de> Co-authored-by: Nicolas Hug <contact@nicolas-hug.com> Co-authored-by: Philip Meier <github.pmeier@posteo.de> Co-authored-by: Nicolas Hug <contact@nicolas-hug.com> Co-authored-by: Nikita Shulga <nshulga@fb.com> Co-authored-by: Sahil Goyal <sahilgoyal6801@gmail.com> Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com> Co-authored-by: Anton Thomma <11010310+thommaa@users.noreply.github.com> Co-authored-by: Anton Thomma <anton@pri.co.nz>
pytorch · Apr 5, 2022 · 0599d33 · 0599d33
1 parent 4c41349
commit 0599d33
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diff --git a/test/builtin_dataset_mocks.py b/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
diff --git a/test/test_prototype_builtin_datasets.py b/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)
diff --git a/torchvision/prototype/datasets/_builtin/__init__.py b/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
diff --git a/torchvision/prototype/datasets/_builtin/usps.py b/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)