From dba46d36aba499d4fb325a55b5ea1baf9785b012 Mon Sep 17 00:00:00 2001 From: vfdev-5 Date: Fri, 24 Feb 2023 16:55:08 +0100 Subject: [PATCH] Fixed uncaught warnings in tests --- test/test_transforms_v2.py | 41 +++++++++++++++++++++++++------------- 1 file changed, 27 insertions(+), 14 deletions(-) diff --git a/test/test_transforms_v2.py b/test/test_transforms_v2.py index 9beded4c957..f5ca976963a 100644 --- a/test/test_transforms_v2.py +++ b/test/test_transforms_v2.py @@ -136,14 +136,14 @@ class TestSmoke: (transforms.RandomCrop([16, 16], pad_if_needed=True), None), (transforms.RandomHorizontalFlip(p=1.0), None), (transforms.RandomPerspective(p=1.0), None), - (transforms.RandomResize(min_size=10, max_size=20), None), - (transforms.RandomResizedCrop([16, 16]), None), + (transforms.RandomResize(min_size=10, max_size=20, antialias=True), None), + (transforms.RandomResizedCrop([16, 16], antialias=True), None), (transforms.RandomRotation(degrees=30), None), - (transforms.RandomShortestSize(min_size=10), None), + (transforms.RandomShortestSize(min_size=10, antialias=True), None), (transforms.RandomVerticalFlip(p=1.0), None), (transforms.RandomZoomOut(p=1.0), None), (transforms.Resize([16, 16], antialias=True), None), - (transforms.ScaleJitter((16, 16), scale_range=(0.8, 1.2)), None), + (transforms.ScaleJitter((16, 16), scale_range=(0.8, 1.2), antialias=True), None), (transforms.ClampBoundingBox(), None), (transforms.ConvertBoundingBoxFormat(datapoints.BoundingBoxFormat.CXCYWH), None), (transforms.ConvertDtype(), None), @@ -1514,7 +1514,7 @@ class TestRandomShortestSize: def test__get_params(self, min_size, max_size, mocker): spatial_size = (3, 10) - transform = transforms.RandomShortestSize(min_size=min_size, max_size=max_size) + transform = transforms.RandomShortestSize(min_size=min_size, max_size=max_size, antialias=True) sample = mocker.MagicMock(spec=datapoints.Image, num_channels=3, spatial_size=spatial_size) params = transform._get_params([sample]) @@ -1595,7 +1595,7 @@ def test__get_params(self): min_size = 3 max_size = 6 - transform = transforms.RandomResize(min_size=min_size, max_size=max_size) + transform = transforms.RandomResize(min_size=min_size, max_size=max_size, antialias=True) for _ in range(10): params = transform._get_params([]) @@ -1791,15 +1791,21 @@ def test_classif_preset(image_type, label_type, dataset_return_type, to_tensor): else: sample = image, label + if to_tensor is transforms.ToTensor: + with pytest.warns(UserWarning, match="deprecated and will be removed"): + to_tensor = to_tensor() + else: + to_tensor = to_tensor() + t = transforms.Compose( [ - transforms.RandomResizedCrop((224, 224)), + transforms.RandomResizedCrop((224, 224), antialias=True), transforms.RandomHorizontalFlip(p=1), transforms.RandAugment(), transforms.TrivialAugmentWide(), transforms.AugMix(), transforms.AutoAugment(), - to_tensor(), + to_tensor, # TODO: ConvertImageDtype is a pass-through on PIL images, is that # intended? This results in a failure if we convert to tensor after # it, because the image would still be uint8 which make Normalize @@ -1830,10 +1836,17 @@ def test_classif_preset(image_type, label_type, dataset_return_type, to_tensor): @pytest.mark.parametrize("sanitize", (True, False)) def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize): torch.manual_seed(0) + + if to_tensor is transforms.ToTensor: + with pytest.warns(UserWarning, match="deprecated and will be removed"): + to_tensor = to_tensor() + else: + to_tensor = to_tensor() + if data_augmentation == "hflip": t = [ transforms.RandomHorizontalFlip(p=1), - to_tensor(), + to_tensor, transforms.ConvertImageDtype(torch.float), ] elif data_augmentation == "lsj": @@ -1847,7 +1860,7 @@ def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize): # ), transforms.RandomCrop((1024, 1024), pad_if_needed=True), transforms.RandomHorizontalFlip(p=1), - to_tensor(), + to_tensor, transforms.ConvertImageDtype(torch.float), ] elif data_augmentation == "multiscale": @@ -1856,7 +1869,7 @@ def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize): min_size=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), max_size=1333, antialias=True ), transforms.RandomHorizontalFlip(p=1), - to_tensor(), + to_tensor, transforms.ConvertImageDtype(torch.float), ] elif data_augmentation == "ssd": @@ -1865,14 +1878,14 @@ def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize): transforms.RandomZoomOut(fill=defaultdict(lambda: (123.0, 117.0, 104.0), {datapoints.Mask: 0})), transforms.RandomIoUCrop(), transforms.RandomHorizontalFlip(p=1), - to_tensor(), + to_tensor, transforms.ConvertImageDtype(torch.float), ] elif data_augmentation == "ssdlite": t = [ transforms.RandomIoUCrop(), transforms.RandomHorizontalFlip(p=1), - to_tensor(), + to_tensor, transforms.ConvertImageDtype(torch.float), ] if sanitize: @@ -1907,7 +1920,7 @@ def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize): out = t(sample) - if to_tensor is transforms.ToTensor and image_type is not datapoints.Image: + if isinstance(to_tensor, transforms.ToTensor) and image_type is not datapoints.Image: assert is_simple_tensor(out["image"]) else: assert isinstance(out["image"], datapoints.Image)