[fbsync] add more KernelInfo's and DispatcherInfo's (#6626)

Summary:
* add KernelInfo for adjust_brightness

* add KernelInfo for adjust_contrast

* add KernelInfo for adjust_hue

* add KernelInfo for adjust_saturation

* add KernelInfo for clamp_bounding_box

* add KernelInfo for {five, ten}_crop_image_tensor as well as skip functionality

* add KernelInfo for normalize

* add KernelInfo for adjust_gamma

* cleanup

* add DispatcherInfo's for previously add KernelInfo's

* add dispatcher info for elastic

Reviewed By: NicolasHug

Differential Revision: D39765294

fbshipit-source-id: 42bdb40ead8807f60fc75fb9259dd34e2d4f0724

Author: jdsgomes

test/prototype_transforms_dispatcher_infos.py

@@ -1,9 +1,9 @@
 import dataclasses
-from typing import Callable, Dict, Type
+from typing import Callable, Dict, Sequence, Type
 
 import pytest
 import torchvision.prototype.transforms.functional as F
-from prototype_transforms_kernel_infos import KERNEL_INFOS
+from prototype_transforms_kernel_infos import KERNEL_INFOS, Skip
 from torchvision.prototype import features
 
 __all__ = ["DispatcherInfo", "DISPATCHER_INFOS"]
@@ -15,6 +15,11 @@
 class DispatcherInfo:
     dispatcher: Callable
     kernels: Dict[Type, Callable]
+    skips: Sequence[Skip] = dataclasses.field(default_factory=list)
+    _skips_map: Dict[str, Skip] = dataclasses.field(default=None, init=False)
+
+    def __post_init__(self):
+        self._skips_map = {skip.test_name: skip for skip in self.skips}
 
     def sample_inputs(self, *types):
         for type in types or self.kernels.keys():
@@ -23,6 +28,11 @@ def sample_inputs(self, *types):
 
             yield from KERNEL_SAMPLE_INPUTS_FN_MAP[self.kernels[type]]()
 
+    def maybe_skip(self, *, test_name, args_kwargs, device):
+        skip = self._skips_map.get(test_name)
+        if skip and skip.condition(args_kwargs, device):
+            pytest.skip(skip.reason)
+
 
 DISPATCHER_INFOS = [
     DispatcherInfo(
@@ -97,6 +107,14 @@ def sample_inputs(self, *types):
             features.Mask: F.perspective_mask,
         },
     ),
+    DispatcherInfo(
+        F.elastic,
+        kernels={
+            features.Image: F.elastic_image_tensor,
+            features.BoundingBox: F.elastic_bounding_box,
+            features.Mask: F.elastic_mask,
+        },
+    ),
     DispatcherInfo(
         F.center_crop,
         kernels={
@@ -153,4 +171,66 @@ def sample_inputs(self, *types):
             features.Image: F.erase_image_tensor,
         },
     ),
+    DispatcherInfo(
+        F.adjust_brightness,
+        kernels={
+            features.Image: F.adjust_brightness_image_tensor,
+        },
+    ),
+    DispatcherInfo(
+        F.adjust_contrast,
+        kernels={
+            features.Image: F.adjust_contrast_image_tensor,
+        },
+    ),
+    DispatcherInfo(
+        F.adjust_gamma,
+        kernels={
+            features.Image: F.adjust_gamma_image_tensor,
+        },
+    ),
+    DispatcherInfo(
+        F.adjust_hue,
+        kernels={
+            features.Image: F.adjust_hue_image_tensor,
+        },
+    ),
+    DispatcherInfo(
+        F.adjust_saturation,
+        kernels={
+            features.Image: F.adjust_saturation_image_tensor,
+        },
+    ),
+    DispatcherInfo(
+        F.five_crop,
+        kernels={
+            features.Image: F.five_crop_image_tensor,
+        },
+        skips=[
+            Skip(
+                "test_scripted_smoke",
+                condition=lambda args_kwargs, device: isinstance(args_kwargs.kwargs["size"], int),
+                reason="Integer size is not supported when scripting five_crop_image_tensor.",
+            ),
+        ],
+    ),
+    DispatcherInfo(
+        F.ten_crop,
+        kernels={
+            features.Image: F.ten_crop_image_tensor,
+        },
+        skips=[
+            Skip(
+                "test_scripted_smoke",
+                condition=lambda args_kwargs, device: isinstance(args_kwargs.kwargs["size"], int),
+                reason="Integer size is not supported when scripting ten_crop_image_tensor.",
+            ),
+        ],
+    ),
+    DispatcherInfo(
+        F.normalize,
+        kernels={
+            features.Image: F.normalize_image_tensor,
+        },
+    ),
 ]