Merge branch 'main' into improved-affine

Author: vfdev-5

test/builtin_dataset_mocks.py

@@ -12,7 +12,7 @@
 import tempfile
 import unittest.mock
 import xml.etree.ElementTree as ET
-from collections import defaultdict, Counter, UserDict
+from collections import defaultdict, Counter
 
 import numpy as np
 import PIL.Image
@@ -34,35 +34,17 @@
 __all__ = ["DATASET_MOCKS", "parametrize_dataset_mocks"]
 
 
-class ResourceMock(datasets.utils.OnlineResource):
-    def __init__(self, *, dataset_name, dataset_config, **kwargs):
-        super().__init__(**kwargs)
-        self.dataset_name = dataset_name
-        self.dataset_config = dataset_config
-
-    def _download(self, _):
-        raise pytest.UsageError(
-            f"Dataset '{self.dataset_name}' requires the file '{self.file_name}' for {self.dataset_config}, "
-            f"but this file does not exist."
-        )
-
-
 class DatasetMock:
-    def __init__(self, name, mock_data_fn, *, configs=None):
+    def __init__(self, name, mock_data_fn):
         self.dataset = find(name)
+        self.info = self.dataset.info
+        self.name = self.info.name
+
         self.root = TEST_HOME / self.dataset.name
         self.mock_data_fn = mock_data_fn
-        self.configs = configs or self.info._configs
+        self.configs = self.info._configs
         self._cache = {}
 
-    @property
-    def info(self):
-        return self.dataset.info
-
-    @property
-    def name(self):
-        return self.info.name
-
     def _parse_mock_data(self, config, mock_infos):
         if mock_infos is None:
             raise pytest.UsageError(
@@ -79,7 +61,7 @@ def _parse_mock_data(self, config, mock_infos):
                 f"returned dictionary uses `DatasetConfig` as key type, all keys should be of that type."
             )
 
-        for config_, mock_info in list(mock_infos.items()):
+        for config_, mock_info in mock_infos.items():
             if config_ in self._cache:
                 raise pytest.UsageError(
                     f"The mock info for config {config_} of dataset {self.name} generated for config {config} "
@@ -103,7 +85,7 @@ def _parse_mock_data(self, config, mock_infos):
         return mock_infos
 
     def _prepare_resources(self, config):
-        with contextlib.suppress(KeyError):
+        if config in self._cache:
             return self._cache[config]
 
         self.root.mkdir(exist_ok=True)
@@ -146,8 +128,6 @@ def parametrize_dataset_mocks(*dataset_mocks, marks=None):
     for mock in dataset_mocks:
         if isinstance(mock, DatasetMock):
             mocks[mock.name] = mock
-        elif isinstance(mock, collections.abc.Sequence):
-            mocks.update({mock_.name: mock_ for mock_ in mock})
         elif isinstance(mock, collections.abc.Mapping):
             mocks.update(mock)
         else:
@@ -173,14 +153,13 @@ def parametrize_dataset_mocks(*dataset_mocks, marks=None):
     )
 
 
-class DatasetMocks(UserDict):
-    def set_from_named_callable(self, fn):
-        name = fn.__name__.replace("_", "-")
-        self.data[name] = DatasetMock(name, fn)
-        return fn
+DATASET_MOCKS = {}
 
 
-DATASET_MOCKS = DatasetMocks()
+def register_mock(fn):
+    name = fn.__name__.replace("_", "-")
+    DATASET_MOCKS[name] = DatasetMock(name, fn)
+    return fn
 
 
 class MNISTMockData:
@@ -258,7 +237,7 @@ def generate(
         return num_samples
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def mnist(info, root, config):
     train = config.split == "train"
     images_file = f"{'train' if train else 't10k'}-images-idx3-ubyte.gz"
@@ -274,7 +253,7 @@ def mnist(info, root, config):
 DATASET_MOCKS.update({name: DatasetMock(name, mnist) for name in ["fashionmnist", "kmnist"]})
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def emnist(info, root, _):
     # The image sets that merge some lower case letters in their respective upper case variant, still use dense
     # labels in the data files. Thus, num_categories != len(categories) there.
@@ -303,7 +282,7 @@ def emnist(info, root, _):
     return mock_infos
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def qmnist(info, root, config):
     num_categories = len(info.categories)
     if config.split == "train":
@@ -382,7 +361,7 @@ def generate(
         make_tar(root, name, folder, compression="gz")
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def cifar10(info, root, config):
     train_files = [f"data_batch_{idx}" for idx in range(1, 6)]
     test_files = ["test_batch"]
@@ -400,7 +379,7 @@ def cifar10(info, root, config):
     return len(train_files if config.split == "train" else test_files)
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def cifar100(info, root, config):
     train_files = ["train"]
     test_files = ["test"]
@@ -418,7 +397,7 @@ def cifar100(info, root, config):
     return len(train_files if config.split == "train" else test_files)
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def caltech101(info, root, config):
     def create_ann_file(root, name):
         import scipy.io
@@ -468,7 +447,7 @@ def create_ann_folder(root, name, file_name_fn, num_examples):
     return num_images_per_category * len(info.categories)
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def caltech256(info, root, config):
     dir = root / "256_ObjectCategories"
     num_images_per_category = 2
@@ -488,7 +467,7 @@ def caltech256(info, root, config):
     return num_images_per_category * len(info.categories)
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def imagenet(info, root, config):
     wnids = tuple(info.extra.wnid_to_category.keys())
     if config.split == "train":
@@ -643,7 +622,7 @@ def generate(
         return num_samples
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def coco(info, root, config):
     return dict(
         zip(
@@ -722,13 +701,13 @@ def generate(cls, root):
         return num_samples_map
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def sbd(info, root, _):
     num_samples_map = SBDMockData.generate(root)
     return {config: num_samples_map[config.split] for config in info._configs}
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def semeion(info, root, config):
     num_samples = 3
 
@@ -839,7 +818,7 @@ def generate(cls, root, *, year, trainval):
         return num_samples_map
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def voc(info, root, config):
     trainval = config.split != "test"
     num_samples_map = VOCMockData.generate(root, year=config.year, trainval=trainval)
@@ -938,13 +917,13 @@ def generate(cls, root):
         return num_samples_map
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def celeba(info, root, _):
     num_samples_map = CelebAMockData.generate(root)
     return {config: num_samples_map[config.split] for config in info._configs}
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def dtd(info, root, _):
     data_folder = root / "dtd"
 
@@ -992,7 +971,7 @@ def dtd(info, root, _):
     return num_samples_map
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def fer2013(info, root, config):
     num_samples = 5 if config.split == "train" else 3
 
@@ -1017,7 +996,7 @@ def fer2013(info, root, config):
     return num_samples
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def gtsrb(info, root, config):
     num_examples_per_class = 5 if config.split == "train" else 3
     classes = ("00000", "00042", "00012")
@@ -1087,7 +1066,7 @@ def _make_ann_file(path, num_examples, class_idx):
     return num_examples
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def clevr(info, root, config):
     data_folder = root / "CLEVR_v1.0"
 
@@ -1193,7 +1172,7 @@ def generate(self, root):
         return num_samples_map
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def oxford_iiit_pet(info, root, config):
     num_samples_map = OxfordIIITPetMockData.generate(root)
     return {config_: num_samples_map[config_.split] for config_ in info._configs}
@@ -1360,13 +1339,13 @@ def generate(cls, root):
         return num_samples_map
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def cub200(info, root, config):
     num_samples_map = (CUB2002011MockData if config.year == "2011" else CUB2002010MockData).generate(root)
     return {config_: num_samples_map[config_.split] for config_ in info._configs if config_.year == config.year}
 
 
-@DATASET_MOCKS.set_from_named_callable
+@register_mock
 def svhn(info, root, config):
     import scipy.io as sio
 

test/test_prototype_builtin_datasets.py

@@ -110,7 +110,7 @@ def test_traversable(self, dataset_mock, config):
             )
         },
     )
-    @pytest.mark.parametrize("annotation_dp_type", (Shuffler, ShardingFilter), ids=lambda type: type.__name__)
+    @pytest.mark.parametrize("annotation_dp_type", (Shuffler, ShardingFilter))
     def test_has_annotations(self, dataset_mock, config, annotation_dp_type):
         def scan(graph):
             for node, sub_graph in graph.items():
@@ -120,10 +120,7 @@ def scan(graph):
         with dataset_mock.prepare(config):
             dataset = datasets.load(dataset_mock.name, **config)
 
-        for dp in scan(traverse(dataset)):
-            if type(dp) is annotation_dp_type:
-                break
-        else:
+        if not any(type(dp) is annotation_dp_type for dp in scan(traverse(dataset))):
             raise AssertionError(f"The dataset doesn't contain a {annotation_dp_type.__name__}() datapipe.")
 
 

torchvision/ops/__init__.py

@@ -13,7 +13,7 @@
 from .deform_conv import deform_conv2d, DeformConv2d
 from .feature_pyramid_network import FeaturePyramidNetwork
 from .focal_loss import sigmoid_focal_loss
-from .generalized_box_iou_loss import generalized_box_iou_loss
+from .giou_loss import generalized_box_iou_loss
 from .misc import FrozenBatchNorm2d, ConvNormActivation, SqueezeExcitation
 from .poolers import MultiScaleRoIAlign
 from .ps_roi_align import ps_roi_align, PSRoIAlign