From b570c64d5b5d56b9f5fa6ebf4d235ea21d7599de Mon Sep 17 00:00:00 2001 From: Francis Lata Date: Wed, 15 Jan 2025 03:33:38 -0800 Subject: [PATCH] temporarily disable openimages dataset tests to debug CI --- test/external/external_test_datasets.py | 126 ++++++++++++------------ 1 file changed, 63 insertions(+), 63 deletions(-) diff --git a/test/external/external_test_datasets.py b/test/external/external_test_datasets.py index 0cd0fceaa8..6b4647827d 100644 --- a/test/external/external_test_datasets.py +++ b/test/external/external_test_datasets.py @@ -83,81 +83,81 @@ class TestKiTS19Dataset(ExternalTestDatasets): np.testing.assert_equal(tinygrad_sample[0][:, 0], ref_sample[0]) np.testing.assert_equal(tinygrad_sample[1], ref_sample[1]) -class TestOpenImagesDataset(ExternalTestDatasets): - def _create_samples(self, subset): - os.makedirs(Path(base_dir:=tempfile.gettempdir() + "/openimages") / f"{subset}/data", exist_ok=True) - os.makedirs(base_dir / Path(f"{subset}/labels"), exist_ok=True) +# class TestOpenImagesDataset(ExternalTestDatasets): +# def _create_samples(self, subset): +# os.makedirs(Path(base_dir:=tempfile.gettempdir() + "/openimages") / f"{subset}/data", exist_ok=True) +# os.makedirs(base_dir / Path(f"{subset}/labels"), exist_ok=True) - lbls, img_size = ["class_1", "class_2"], (447, 1024) - cats = [{"id": i, "name": c, "supercategory": None} for i, c in enumerate(lbls)] - imgs = [ - { - "id": i, "file_name": f"image_{i}.jpg", - "height": img_size[0], "width": img_size[1], - "subset": subset, "license": None, "coco_url": None - } - for i in range(len(lbls)) - ] - annots = [ - { - "id": i, "image_id": i, - "category_id": 0, "bbox": [23.217183744, 31.75409775, 964.1241282560001, 326.09017434000003], - "area": 314391.4050683996, "IsOccluded": 0, - "IsInside": 0, "IsDepiction": 0, - "IsTruncated": 0, "IsGroupOf": 0, - "iscrowd": 0 - } - for i in range(len(lbls)) - ] - info = {"dataset": "openimages_mlperf", "version": "v6"} - coco_annotations = {"info": info, "licenses": [], "categories": cats, "images": imgs, "annotations": annots} +# lbls, img_size = ["class_1", "class_2"], (447, 1024) +# cats = [{"id": i, "name": c, "supercategory": None} for i, c in enumerate(lbls)] +# imgs = [ +# { +# "id": i, "file_name": f"image_{i}.jpg", +# "height": img_size[0], "width": img_size[1], +# "subset": subset, "license": None, "coco_url": None +# } +# for i in range(len(lbls)) +# ] +# annots = [ +# { +# "id": i, "image_id": i, +# "category_id": 0, "bbox": [23.217183744, 31.75409775, 964.1241282560001, 326.09017434000003], +# "area": 314391.4050683996, "IsOccluded": 0, +# "IsInside": 0, "IsDepiction": 0, +# "IsTruncated": 0, "IsGroupOf": 0, +# "iscrowd": 0 +# } +# for i in range(len(lbls)) +# ] +# info = {"dataset": "openimages_mlperf", "version": "v6"} +# coco_annotations = {"info": info, "licenses": [], "categories": cats, "images": imgs, "annotations": annots} - with open(ann_file:=base_dir / Path(f"{subset}/labels/openimages-mlperf.json"), "w") as fp: - json.dump(coco_annotations, fp) +# with open(ann_file:=base_dir / Path(f"{subset}/labels/openimages-mlperf.json"), "w") as fp: +# json.dump(coco_annotations, fp) - for i in range(len(lbls)): - img = Image.new("RGB", img_size[::-1]) - img.save(base_dir / Path(f"{subset}/data/image_{i}.jpg")) +# for i in range(len(lbls)): +# img = Image.new("RGB", img_size[::-1]) +# img.save(base_dir / Path(f"{subset}/data/image_{i}.jpg")) - return base_dir, ann_file +# return base_dir, ann_file - def _create_ref_dataloader(self, subset): - self._set_seed() - base_dir, ann_file = self._create_samples(subset) - transforms = DetectionPresetTrain("hflip") if subset == "train" else DetectionPresetEval() - dataset = get_openimages(ann_file.stem, base_dir, subset, transforms) - return iter(dataset) +# def _create_ref_dataloader(self, subset): +# self._set_seed() +# base_dir, ann_file = self._create_samples(subset) +# transforms = DetectionPresetTrain("hflip") if subset == "train" else DetectionPresetEval() +# dataset = get_openimages(ann_file.stem, base_dir, subset, transforms) +# return iter(dataset) - def _create_tinygrad_dataloader(self, subset, anchors, batch_size=1, seed=42): - base_dir, ann_file = self._create_samples(subset) - dataset = COCO(ann_file) - dataloader = batch_load_retinanet(dataset, subset == "validation", anchors, Path(base_dir), batch_size=batch_size, shuffle=False, seed=seed) - return iter(dataloader) +# def _create_tinygrad_dataloader(self, subset, anchors, batch_size=1, seed=42): +# base_dir, ann_file = self._create_samples(subset) +# dataset = COCO(ann_file) +# dataloader = batch_load_retinanet(dataset, subset == "validation", anchors, Path(base_dir), batch_size=batch_size, shuffle=False, seed=seed) +# return iter(dataloader) - def test_training_set(self): - img_size, img_mean, img_std, anchors = (800, 800), [0.0, 0.0, 0.0], [1.0, 1.0, 1.0], torch.ones((120087, 4)) - tinygrad_dataloader, ref_dataloader = self._create_tinygrad_dataloader("train", anchors.numpy()), self._create_ref_dataloader("train") - transform = GeneralizedRCNNTransform(img_size, img_mean, img_std) +# def test_training_set(self): +# img_size, img_mean, img_std, anchors = (800, 800), [0.0, 0.0, 0.0], [1.0, 1.0, 1.0], torch.ones((120087, 4)) +# tinygrad_dataloader, ref_dataloader = self._create_tinygrad_dataloader("train", anchors.numpy()), self._create_ref_dataloader("train") +# transform = GeneralizedRCNNTransform(img_size, img_mean, img_std) - for ((tinygrad_img, tinygrad_boxes, tinygrad_labels, _, _), (ref_img, ref_tgt)) in zip(tinygrad_dataloader, ref_dataloader): - ref_tgt = [ref_tgt] +# for ((tinygrad_img, tinygrad_boxes, tinygrad_labels, _, _), (ref_img, ref_tgt)) in zip(tinygrad_dataloader, ref_dataloader): +# ref_tgt = [ref_tgt] - ref_img, ref_tgt = transform(ref_img.unsqueeze(0), ref_tgt) - ref_tgt = postprocess_targets(ref_tgt, anchors.unsqueeze(0)) - ref_boxes, ref_labels = ref_tgt[0]["boxes"], ref_tgt[0]["labels"] +# ref_img, ref_tgt = transform(ref_img.unsqueeze(0), ref_tgt) +# ref_tgt = postprocess_targets(ref_tgt, anchors.unsqueeze(0)) +# ref_boxes, ref_labels = ref_tgt[0]["boxes"], ref_tgt[0]["labels"] - np.testing.assert_equal(tinygrad_img.numpy(), ref_img.tensors.transpose(1, 3).numpy()) - np.testing.assert_equal(tinygrad_boxes[0].numpy(), ref_boxes.numpy()) - np.testing.assert_equal(tinygrad_labels[0].numpy(), ref_labels.numpy()) +# np.testing.assert_equal(tinygrad_img.numpy(), ref_img.tensors.transpose(1, 3).numpy()) +# np.testing.assert_equal(tinygrad_boxes[0].numpy(), ref_boxes.numpy()) +# np.testing.assert_equal(tinygrad_labels[0].numpy(), ref_labels.numpy()) - def test_validation_set(self): - img_size, img_mean, img_std, anchors = (800, 800), [0.0, 0.0, 0.0], [1.0, 1.0, 1.0], torch.ones((120087, 4)) - tinygrad_dataloader, ref_dataloader = self._create_tinygrad_dataloader("validation", anchors.numpy()), self._create_ref_dataloader("val") - transform = GeneralizedRCNNTransform(img_size, img_mean, img_std) +# def test_validation_set(self): +# img_size, img_mean, img_std, anchors = (800, 800), [0.0, 0.0, 0.0], [1.0, 1.0, 1.0], torch.ones((120087, 4)) +# tinygrad_dataloader, ref_dataloader = self._create_tinygrad_dataloader("validation", anchors.numpy()), self._create_ref_dataloader("val") +# transform = GeneralizedRCNNTransform(img_size, img_mean, img_std) - for ((tinygrad_img, _), (ref_img, _)) in zip(tinygrad_dataloader, ref_dataloader): - ref_img, _ = transform(ref_img.unsqueeze(0)) - np.testing.assert_equal(tinygrad_img.numpy(), ref_img.tensors.transpose(1, 3).numpy()) +# for ((tinygrad_img, _), (ref_img, _)) in zip(tinygrad_dataloader, ref_dataloader): +# ref_img, _ = transform(ref_img.unsqueeze(0)) +# np.testing.assert_equal(tinygrad_img.numpy(), ref_img.tensors.transpose(1, 3).numpy()) if __name__ == '__main__': unittest.main()