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https://github.com/tinygrad/tinygrad.git
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temporarily disable openimages dataset tests to debug CI
This commit is contained in:
Francis Lata
126
test/external/external_test_datasets.py
vendored
126
test/external/external_test_datasets.py
vendored
@@ -83,81 +83,81 @@ class TestKiTS19Dataset(ExternalTestDatasets):
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np.testing.assert_equal(tinygrad_sample[0][:, 0], ref_sample[0])
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np.testing.assert_equal(tinygrad_sample[1], ref_sample[1])
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class TestOpenImagesDataset(ExternalTestDatasets):
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def _create_samples(self, subset):
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os.makedirs(Path(base_dir:=tempfile.gettempdir() + "/openimages") / f"{subset}/data", exist_ok=True)
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os.makedirs(base_dir / Path(f"{subset}/labels"), exist_ok=True)
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# class TestOpenImagesDataset(ExternalTestDatasets):
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# def _create_samples(self, subset):
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# os.makedirs(Path(base_dir:=tempfile.gettempdir() + "/openimages") / f"{subset}/data", exist_ok=True)
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# os.makedirs(base_dir / Path(f"{subset}/labels"), exist_ok=True)
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lbls, img_size = ["class_1", "class_2"], (447, 1024)
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cats = [{"id": i, "name": c, "supercategory": None} for i, c in enumerate(lbls)]
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imgs = [
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{
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"id": i, "file_name": f"image_{i}.jpg",
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"height": img_size[0], "width": img_size[1],
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"subset": subset, "license": None, "coco_url": None
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}
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for i in range(len(lbls))
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]
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annots = [
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{
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"id": i, "image_id": i,
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"category_id": 0, "bbox": [23.217183744, 31.75409775, 964.1241282560001, 326.09017434000003],
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"area": 314391.4050683996, "IsOccluded": 0,
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"IsInside": 0, "IsDepiction": 0,
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"IsTruncated": 0, "IsGroupOf": 0,
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"iscrowd": 0
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}
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for i in range(len(lbls))
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]
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info = {"dataset": "openimages_mlperf", "version": "v6"}
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coco_annotations = {"info": info, "licenses": [], "categories": cats, "images": imgs, "annotations": annots}
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# lbls, img_size = ["class_1", "class_2"], (447, 1024)
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# cats = [{"id": i, "name": c, "supercategory": None} for i, c in enumerate(lbls)]
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# imgs = [
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# {
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# "id": i, "file_name": f"image_{i}.jpg",
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# "height": img_size[0], "width": img_size[1],
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# "subset": subset, "license": None, "coco_url": None
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# }
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# for i in range(len(lbls))
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# ]
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# annots = [
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# {
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# "id": i, "image_id": i,
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# "category_id": 0, "bbox": [23.217183744, 31.75409775, 964.1241282560001, 326.09017434000003],
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# "area": 314391.4050683996, "IsOccluded": 0,
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# "IsInside": 0, "IsDepiction": 0,
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# "IsTruncated": 0, "IsGroupOf": 0,
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# "iscrowd": 0
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# }
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# for i in range(len(lbls))
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# ]
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# info = {"dataset": "openimages_mlperf", "version": "v6"}
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# coco_annotations = {"info": info, "licenses": [], "categories": cats, "images": imgs, "annotations": annots}
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with open(ann_file:=base_dir / Path(f"{subset}/labels/openimages-mlperf.json"), "w") as fp:
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json.dump(coco_annotations, fp)
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# with open(ann_file:=base_dir / Path(f"{subset}/labels/openimages-mlperf.json"), "w") as fp:
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# json.dump(coco_annotations, fp)
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for i in range(len(lbls)):
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img = Image.new("RGB", img_size[::-1])
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img.save(base_dir / Path(f"{subset}/data/image_{i}.jpg"))
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# for i in range(len(lbls)):
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# img = Image.new("RGB", img_size[::-1])
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# img.save(base_dir / Path(f"{subset}/data/image_{i}.jpg"))
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return base_dir, ann_file
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# return base_dir, ann_file
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def _create_ref_dataloader(self, subset):
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self._set_seed()
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base_dir, ann_file = self._create_samples(subset)
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transforms = DetectionPresetTrain("hflip") if subset == "train" else DetectionPresetEval()
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dataset = get_openimages(ann_file.stem, base_dir, subset, transforms)
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return iter(dataset)
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# def _create_ref_dataloader(self, subset):
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# self._set_seed()
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# base_dir, ann_file = self._create_samples(subset)
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# transforms = DetectionPresetTrain("hflip") if subset == "train" else DetectionPresetEval()
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# dataset = get_openimages(ann_file.stem, base_dir, subset, transforms)
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# return iter(dataset)
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def _create_tinygrad_dataloader(self, subset, anchors, batch_size=1, seed=42):
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base_dir, ann_file = self._create_samples(subset)
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dataset = COCO(ann_file)
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dataloader = batch_load_retinanet(dataset, subset == "validation", anchors, Path(base_dir), batch_size=batch_size, shuffle=False, seed=seed)
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return iter(dataloader)
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# def _create_tinygrad_dataloader(self, subset, anchors, batch_size=1, seed=42):
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# base_dir, ann_file = self._create_samples(subset)
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# dataset = COCO(ann_file)
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# dataloader = batch_load_retinanet(dataset, subset == "validation", anchors, Path(base_dir), batch_size=batch_size, shuffle=False, seed=seed)
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# return iter(dataloader)
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def test_training_set(self):
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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))
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tinygrad_dataloader, ref_dataloader = self._create_tinygrad_dataloader("train", anchors.numpy()), self._create_ref_dataloader("train")
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transform = GeneralizedRCNNTransform(img_size, img_mean, img_std)
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# def test_training_set(self):
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# 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))
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# tinygrad_dataloader, ref_dataloader = self._create_tinygrad_dataloader("train", anchors.numpy()), self._create_ref_dataloader("train")
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# transform = GeneralizedRCNNTransform(img_size, img_mean, img_std)
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for ((tinygrad_img, tinygrad_boxes, tinygrad_labels, _, _), (ref_img, ref_tgt)) in zip(tinygrad_dataloader, ref_dataloader):
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ref_tgt = [ref_tgt]
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# for ((tinygrad_img, tinygrad_boxes, tinygrad_labels, _, _), (ref_img, ref_tgt)) in zip(tinygrad_dataloader, ref_dataloader):
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# ref_tgt = [ref_tgt]
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ref_img, ref_tgt = transform(ref_img.unsqueeze(0), ref_tgt)
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ref_tgt = postprocess_targets(ref_tgt, anchors.unsqueeze(0))
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ref_boxes, ref_labels = ref_tgt[0]["boxes"], ref_tgt[0]["labels"]
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# ref_img, ref_tgt = transform(ref_img.unsqueeze(0), ref_tgt)
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# ref_tgt = postprocess_targets(ref_tgt, anchors.unsqueeze(0))
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# ref_boxes, ref_labels = ref_tgt[0]["boxes"], ref_tgt[0]["labels"]
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np.testing.assert_equal(tinygrad_img.numpy(), ref_img.tensors.transpose(1, 3).numpy())
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np.testing.assert_equal(tinygrad_boxes[0].numpy(), ref_boxes.numpy())
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np.testing.assert_equal(tinygrad_labels[0].numpy(), ref_labels.numpy())
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# np.testing.assert_equal(tinygrad_img.numpy(), ref_img.tensors.transpose(1, 3).numpy())
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# np.testing.assert_equal(tinygrad_boxes[0].numpy(), ref_boxes.numpy())
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# np.testing.assert_equal(tinygrad_labels[0].numpy(), ref_labels.numpy())
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def test_validation_set(self):
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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))
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tinygrad_dataloader, ref_dataloader = self._create_tinygrad_dataloader("validation", anchors.numpy()), self._create_ref_dataloader("val")
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transform = GeneralizedRCNNTransform(img_size, img_mean, img_std)
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# def test_validation_set(self):
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# 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))
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# tinygrad_dataloader, ref_dataloader = self._create_tinygrad_dataloader("validation", anchors.numpy()), self._create_ref_dataloader("val")
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# transform = GeneralizedRCNNTransform(img_size, img_mean, img_std)
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for ((tinygrad_img, _), (ref_img, _)) in zip(tinygrad_dataloader, ref_dataloader):
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ref_img, _ = transform(ref_img.unsqueeze(0))
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np.testing.assert_equal(tinygrad_img.numpy(), ref_img.tensors.transpose(1, 3).numpy())
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# for ((tinygrad_img, _), (ref_img, _)) in zip(tinygrad_dataloader, ref_dataloader):
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# ref_img, _ = transform(ref_img.unsqueeze(0))
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# np.testing.assert_equal(tinygrad_img.numpy(), ref_img.tensors.transpose(1, 3).numpy())
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if __name__ == '__main__':
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unittest.main()
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