Merge branch 'master' into tests-lsun

test/datasets_utils.py

@@ -6,6 +6,8 @@
 import itertools
 import os
 import pathlib
+import random
+import string
 import unittest
 import unittest.mock
 from typing import Any, Callable, Dict, Iterator, List, Optional, Sequence, Tuple, Union
@@ -32,6 +34,7 @@
     "create_image_folder",
     "create_video_file",
     "create_video_folder",
+    "create_random_string",
 ]
 
 
@@ -93,14 +96,6 @@ def inner_wrapper(*args, **kwargs):
     return outer_wrapper
 
 
-# As of Python 3.7 this is provided by contextlib
-# https://docs.python.org/3.7/library/contextlib.html#contextlib.nullcontext
-# TODO: If the minimum Python requirement is >= 3.7, replace this
-@contextlib.contextmanager
-def nullcontext(enter_result=None):
-    yield enter_result
-
-
 def test_all_configs(test):
     """Decorator to run test against all configurations.
 
@@ -116,7 +111,7 @@ def test_foo(self, config):
 
     @functools.wraps(test)
     def wrapper(self):
-        for config in self.CONFIGS:
+        for config in self.CONFIGS or (self._DEFAULT_CONFIG,):
             with self.subTest(**config):
                 test(self, config)
 
@@ -207,6 +202,8 @@ def test_baz(self):
     CONFIGS = None
     REQUIRED_PACKAGES = None
 
+    _DEFAULT_CONFIG = None
+
     _TRANSFORM_KWARGS = {
         "transform",
         "target_transform",
@@ -268,7 +265,7 @@ def create_dataset(
         self,
         config: Optional[Dict[str, Any]] = None,
         inject_fake_data: bool = True,
-        disable_download_extract: Optional[bool] = None,
+        patch_checks: Optional[bool] = None,
         **kwargs: Any,
     ) -> Iterator[Tuple[torchvision.datasets.VisionDataset, Dict[str, Any]]]:
         r"""Create the dataset in a temporary directory.
@@ -278,8 +275,8 @@ def create_dataset(
                 default configuration is used.
             inject_fake_data (bool): If ``True`` (default) inject the fake data with :meth:`.inject_fake_data` before
                 creating the dataset.
-            disable_download_extract (Optional[bool]): If ``True`` disable download and extract logic while creating
-                the dataset. If ``None`` (default) this takes the same value as ``inject_fake_data``.
+            patch_checks (Optional[bool]): If ``True`` disable integrity check logic while creating the dataset. If
+                omitted defaults to the same value as ``inject_fake_data``.
             **kwargs (Any): Additional parameters passed to the dataset. These parameters take precedence in case they
                 overlap with ``config``.
 
@@ -288,43 +285,28 @@ def create_dataset(
             info (Dict[str, Any]): Additional information about the injected fake data. See :meth:`.inject_fake_data`
                 for details.
         """
-        if config is None:
-            config = self.CONFIGS[0].copy()
+        default_config = self._DEFAULT_CONFIG.copy()
+        if config is not None:
+            default_config.update(config)
+        config = default_config
+
+        if patch_checks is None:
+            patch_checks = inject_fake_data
 
         special_kwargs, other_kwargs = self._split_kwargs(kwargs)
+        if "download" in self._HAS_SPECIAL_KWARG:
+            special_kwargs["download"] = False
         config.update(other_kwargs)
 
-        if disable_download_extract is None:
-            disable_download_extract = inject_fake_data
+        patchers = self._patch_download_extract()
+        if patch_checks:
+            patchers.update(self._patch_checks())
 
         with get_tmp_dir() as tmpdir:
             args = self.dataset_args(tmpdir, config)
+            info = self._inject_fake_data(tmpdir, config) if inject_fake_data else None
 
-            if inject_fake_data:
-                info = self.inject_fake_data(tmpdir, config)
-                if info is None:
-                    raise UsageError(
-                        "The method 'inject_fake_data' needs to return at least an integer indicating the number of "
-                        "examples for the current configuration."
-                    )
-                elif isinstance(info, int):
-                    info = dict(num_examples=info)
-                elif not isinstance(info, dict):
-                    raise UsageError(
-                        f"The additional information returned by the method 'inject_fake_data' must be either an "
-                        f"integer indicating the number of examples for the current configuration or a dictionary with "
-                        f"the same content. Got {type(info)} instead."
-                    )
-                elif "num_examples" not in info:
-                    raise UsageError(
-                        "The information dictionary returned by the method 'inject_fake_data' must contain a "
-                        "'num_examples' field that holds the number of examples for the current configuration."
-                    )
-            else:
-                info = None
-
-            cm = self._disable_download_extract if disable_download_extract else nullcontext
-            with cm(special_kwargs), disable_console_output():
+            with self._maybe_apply_patches(patchers), disable_console_output():
                 dataset = self.DATASET_CLASS(*args, **config, **special_kwargs)
 
             yield dataset, info
@@ -352,19 +334,17 @@ def _verify_required_public_class_attributes(cls):
     @classmethod
     def _populate_private_class_attributes(cls):
         argspec = inspect.getfullargspec(cls.DATASET_CLASS.__init__)
+
+        cls._DEFAULT_CONFIG = {
+            kwarg: default
+            for kwarg, default in zip(argspec.args[-len(argspec.defaults):], argspec.defaults)
+            if kwarg not in cls._SPECIAL_KWARGS
+        }
+
         cls._HAS_SPECIAL_KWARG = {name for name in cls._SPECIAL_KWARGS if name in argspec.args}
 
     @classmethod
     def _process_optional_public_class_attributes(cls):
-        argspec = inspect.getfullargspec(cls.DATASET_CLASS.__init__)
-        if cls.CONFIGS is None:
-            config = {
-                kwarg: default
-                for kwarg, default in zip(argspec.args[-len(argspec.defaults):], argspec.defaults)
-                if kwarg not in cls._SPECIAL_KWARGS
-            }
-            cls.CONFIGS = (config,)
-
         if cls.REQUIRED_PACKAGES is not None:
             try:
                 for pkg in cls.REQUIRED_PACKAGES:
@@ -380,28 +360,44 @@ def _split_kwargs(self, kwargs):
         other_kwargs = {key: special_kwargs.pop(key) for key in set(special_kwargs.keys()) - self._SPECIAL_KWARGS}
         return special_kwargs, other_kwargs
 
-    @contextlib.contextmanager
-    def _disable_download_extract(self, special_kwargs):
-        inject_download_kwarg = "download" in self._HAS_SPECIAL_KWARG and "download" not in special_kwargs
-        if inject_download_kwarg:
-            special_kwargs["download"] = False
+    def _inject_fake_data(self, tmpdir, config):
+        info = self.inject_fake_data(tmpdir, config)
+        if info is None:
+            raise UsageError(
+                "The method 'inject_fake_data' needs to return at least an integer indicating the number of "
+                "examples for the current configuration."
+            )
+        elif isinstance(info, int):
+            info = dict(num_examples=info)
+        elif not isinstance(info, dict):
+            raise UsageError(
+                f"The additional information returned by the method 'inject_fake_data' must be either an "
+                f"integer indicating the number of examples for the current configuration or a dictionary with "
+                f"the same content. Got {type(info)} instead."
+            )
+        elif "num_examples" not in info:
+            raise UsageError(
+                "The information dictionary returned by the method 'inject_fake_data' must contain a "
+                "'num_examples' field that holds the number of examples for the current configuration."
+            )
+        return info
+
+    def _patch_download_extract(self):
+        module = inspect.getmodule(self.DATASET_CLASS).__name__
+        return {unittest.mock.patch(f"{module}.{function}") for function in self._DOWNLOAD_EXTRACT_FUNCTIONS}
 
+    def _patch_checks(self):
         module = inspect.getmodule(self.DATASET_CLASS).__name__
+        return {unittest.mock.patch(f"{module}.{function}", return_value=True) for function in self._CHECK_FUNCTIONS}
+
+    @contextlib.contextmanager
+    def _maybe_apply_patches(self, patchers):
         with contextlib.ExitStack() as stack:
             mocks = {}
-            for function, kwargs in itertools.chain(
-                zip(self._CHECK_FUNCTIONS, [dict(return_value=True)] * len(self._CHECK_FUNCTIONS)),
-                zip(self._DOWNLOAD_EXTRACT_FUNCTIONS, [dict()] * len(self._DOWNLOAD_EXTRACT_FUNCTIONS)),
-            ):
+            for patcher in patchers:
                 with contextlib.suppress(AttributeError):
-                    patcher = unittest.mock.patch(f"{module}.{function}", **kwargs)
-                    mocks[function] = stack.enter_context(patcher)
-
-            try:
-                yield mocks
-            finally:
-                if inject_download_kwarg:
-                    del special_kwargs["download"]
+                    mocks[patcher.target] = stack.enter_context(patcher)
+            yield mocks
 
     def test_not_found_or_corrupted(self):
         with self.assertRaises((FileNotFoundError, RuntimeError)):
@@ -469,13 +465,13 @@ def create_dataset(
         self,
         config: Optional[Dict[str, Any]] = None,
         inject_fake_data: bool = True,
-        disable_download_extract: Optional[bool] = None,
+        patch_checks: Optional[bool] = None,
         **kwargs: Any,
     ) -> Iterator[Tuple[torchvision.datasets.VisionDataset, Dict[str, Any]]]:
         with super().create_dataset(
             config=config,
             inject_fake_data=inject_fake_data,
-            disable_download_extract=disable_download_extract,
+            patch_checks=patch_checks,
             **kwargs,
         ) as (dataset, info):
             # PIL.Image.open() only loads the image meta data upfront and keeps the file open until the first access
@@ -572,7 +568,7 @@ def create_image_file(
 
     image = create_image_or_video_tensor(size)
     file = pathlib.Path(root) / name
-    PIL.Image.fromarray(image.permute(2, 1, 0).numpy()).save(file)
+    PIL.Image.fromarray(image.permute(2, 1, 0).numpy()).save(file, **kwargs)
     return file
 
 
@@ -708,6 +704,21 @@ def size(idx):
     os.makedirs(root)
 
     return [
-        create_video_file(root, file_name_fn(idx), size=size(idx) if callable(size) else size)
+        create_video_file(root, file_name_fn(idx), size=size(idx) if callable(size) else size, **kwargs)
         for idx in range(num_examples)
     ]
+
+
+def create_random_string(length: int, *digits: str) -> str:
+    """Create a random string.
+
+    Args:
+        length (int): Number of characters in the generated string.
+        *characters (str): Characters to sample from. If omitted defaults to :attr:`string.ascii_lowercase`.
+    """
+    if not digits:
+        digits = string.ascii_lowercase
+    else:
+        digits = "".join(itertools.chain(*digits))
+
+    return "".join(random.choice(digits) for _ in range(length))

torchvision/datasets/phototour.py

@@ -10,7 +10,17 @@
 
 
 class PhotoTour(VisionDataset):
-    """`Learning Local Image Descriptors Data <http://phototour.cs.washington.edu/patches/default.htm>`_ Dataset.
+    """`Multi-view Stereo Correspondence <http://matthewalunbrown.com/patchdata/patchdata.html>`_ Dataset.
+
+    .. note::
+
+        We only provide the newer version of the dataset, since the authors state that it
+
+            is more suitable for training descriptors based on difference of Gaussian, or Harris corners, as the
+            patches are centred on real interest point detections, rather than being projections of 3D points as is the
+            case in the old dataset.
+
+        The original dataset is available under http://phototour.cs.washington.edu/patches/default.htm.
 
 
     Args: