GPU jpeg decoder: add batch support and hardware decoding

benchmarks/encoding_decoding.py

@@ -0,0 +1,99 @@
+import os
+import platform
+import statistics
+
+import torch
+import torch.utils.benchmark as benchmark
+import torchvision
+
+
+def print_machine_specs():
+    print("Processor:", platform.processor())
+    print("Platform:", platform.platform())
+    print("Logical CPUs:", os.cpu_count())
+    print(f"\nCUDA device: {torch.cuda.get_device_name()}")
+    print(f"Total Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")
+
+
+def get_data():
+    transform = torchvision.transforms.Compose(
+        [
+            torchvision.transforms.PILToTensor(),
+        ]
+    )
+    path = os.path.join(os.getcwd(), "data")
+    testset = torchvision.datasets.Places365(
+        root="./data", download=not os.path.exists(path), transform=transform, split="val"
+    )
+    testloader = torch.utils.data.DataLoader(
+        testset, batch_size=1000, shuffle=False, num_workers=1, collate_fn=lambda batch: [r[0] for r in batch]
+    )
+    return next(iter(testloader))
+
+
+def run_encoding_benchmark(decoded_images):
+    results = []
+    for device in ["cpu", "cuda"]:
+        decoded_images_device = [t.to(device=device) for t in decoded_images]
+        for size in [1, 100, 1000]:
+            for num_threads in [1, 12, 24]:
+                for stmt, strat in zip(
+                    [
+                        "[torchvision.io.encode_jpeg(img) for img in decoded_images_device_trunc]",
+                        "torchvision.io.encode_jpeg(decoded_images_device_trunc)",
+                    ],
+                    ["unfused", "fused"],
+                ):
+                    decoded_images_device_trunc = decoded_images_device[:size]
+                    t = benchmark.Timer(
+                        stmt=stmt,
+                        setup="import torchvision",
+                        globals={"decoded_images_device_trunc": decoded_images_device_trunc},
+                        label="Image Encoding",
+                        sub_label=f"{device.upper()} ({strat}): {stmt}",
+                        description=f"{size} images",
+                        num_threads=num_threads,
+                    )
+                    results.append(t.blocked_autorange())
+    compare = benchmark.Compare(results)
+    compare.print()
+
+
+def run_decoding_benchmark(encoded_images):
+    results = []
+    for device in ["cpu", "cuda"]:
+        for size in [1, 100, 1000]:
+            for num_threads in [1, 12, 24]:
+                for stmt, strat in zip(
+                    [
+                        f"[torchvision.io.decode_jpeg(img, device='{device}') for img in encoded_images_trunc]",
+                        f"torchvision.io.decode_jpeg(encoded_images_trunc, device='{device}')",
+                    ],
+                    ["unfused", "fused"],
+                ):
+                    encoded_images_trunc = encoded_images[:size]
+                    t = benchmark.Timer(
+                        stmt=stmt,
+                        setup="import torchvision",
+                        globals={"encoded_images_trunc": encoded_images_trunc},
+                        label="Image Decoding",
+                        sub_label=f"{device.upper()} ({strat}): {stmt}",
+                        description=f"{size} images",
+                        num_threads=num_threads,
+                    )
+                    results.append(t.blocked_autorange())
+    compare = benchmark.Compare(results)
+    compare.print()
+
+
+if __name__ == "__main__":
+    print_machine_specs()
+    decoded_images = get_data()
+    mean_h, mean_w = statistics.mean(t.shape[-2] for t in decoded_images), statistics.mean(
+        t.shape[-1] for t in decoded_images
+    )
+    print(f"\nMean image size: {int(mean_h)}x{int(mean_w)}")
+    run_encoding_benchmark(decoded_images)
+    encoded_images_cuda = torchvision.io.encode_jpeg([img.cuda() for img in decoded_images])
+    encoded_images_cpu = [img.cpu() for img in encoded_images_cuda]
+    run_decoding_benchmark(encoded_images_cpu)

test/test_image.py

@@ -413,23 +413,32 @@ def test_read_interlaced_png():
 
 
 @needs_cuda
-@pytest.mark.parametrize(
-    "img_path",
-    [pytest.param(jpeg_path, id=_get_safe_image_name(jpeg_path)) for jpeg_path in get_images(IMAGE_ROOT, ".jpg")],
-)
 @pytest.mark.parametrize("mode", [ImageReadMode.UNCHANGED, ImageReadMode.GRAY, ImageReadMode.RGB])
 @pytest.mark.parametrize("scripted", (False, True))
-def test_decode_jpeg_cuda(mode, img_path, scripted):
-    if "cmyk" in img_path:
-        pytest.xfail("Decoding a CMYK jpeg isn't supported")
+def test_decode_jpegs_cuda(mode, scripted):
+    encoded_images = []
+    for jpeg_path in get_images(IMAGE_ROOT, ".jpg"):
+        if "cmyk" in jpeg_path:
+            continue
+        encoded_image = read_file(jpeg_path)
+        encoded_images.append(encoded_image)
+    decoded_images_cpu = decode_jpeg(encoded_images, mode=mode)
+    decode_fn = torch.jit.script(decode_jpeg) if scripted else decode_jpeg
 
-    data = read_file(img_path)
-    img = decode_image(data, mode=mode)
-    f = torch.jit.script(decode_jpeg) if scripted else decode_jpeg
-    img_nvjpeg = f(data, mode=mode, device="cuda")
+    # test multithreaded decoding
+    # in the current version we prevent this by using a lock but we still want to test it
+    num_workers = 10
 
-    # Some difference expected between jpeg implementations
-    assert (img.float() - img_nvjpeg.cpu().float()).abs().mean() < 2
+    with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:
+        futures = [executor.submit(decode_fn, encoded_images, mode, "cuda") for _ in range(num_workers)]
+    decoded_images_threaded = [future.result() for future in futures]
+    assert len(decoded_images_threaded) == num_workers
+    for decoded_images in decoded_images_threaded:
+        assert len(decoded_images) == len(encoded_images)
+        for decoded_image_cuda, decoded_image_cpu in zip(decoded_images, decoded_images_cpu):
+            assert decoded_image_cuda.shape == decoded_image_cpu.shape
+            assert decoded_image_cuda.dtype == decoded_image_cpu.dtype == torch.uint8
+            assert (decoded_image_cuda.cpu().float() - decoded_image_cpu.cpu().float()).abs().mean() < 2
 
 
 @needs_cuda
@@ -440,25 +449,95 @@ def test_decode_image_cuda_raises():
 
 
 @needs_cuda
-@pytest.mark.parametrize("cuda_device", ("cuda", "cuda:0", torch.device("cuda")))
-def test_decode_jpeg_cuda_device_param(cuda_device):
-    """Make sure we can pass a string or a torch.device as device param"""
+def test_decode_jpeg_cuda_device_param():
     path = next(path for path in get_images(IMAGE_ROOT, ".jpg") if "cmyk" not in path)
     data = read_file(path)
-    decode_jpeg(data, device=cuda_device)
+    current_device = torch.cuda.current_device()
+    current_stream = torch.cuda.current_stream()
+    num_devices = torch.cuda.device_count()
+    devices = ["cuda", torch.device("cuda")] + [torch.device(f"cuda:{i}") for i in range(num_devices)]
+    results = []
+    for device in devices:
+        results.append(decode_jpeg(data, device=device))
+    assert len(results) == len(devices)
+    for result in results:
+        assert torch.all(result.cpu() == results[0].cpu())
+    assert current_device == torch.cuda.current_device()
+    assert current_stream == torch.cuda.current_stream()
 
 
 @needs_cuda
 def test_decode_jpeg_cuda_errors():
     data = read_file(next(get_images(IMAGE_ROOT, ".jpg")))
     with pytest.raises(RuntimeError, match="Expected a non empty 1-dimensional tensor"):
         decode_jpeg(data.reshape(-1, 1), device="cuda")
-    with pytest.raises(RuntimeError, match="input tensor must be on CPU"):
+    with pytest.raises(ValueError, match="must be tensors"):
+        decode_jpeg([1, 2, 3])
+    with pytest.raises(ValueError, match="Input tensor must be a CPU tensor"):
         decode_jpeg(data.to("cuda"), device="cuda")
     with pytest.raises(RuntimeError, match="Expected a torch.uint8 tensor"):
         decode_jpeg(data.to(torch.float), device="cuda")
-    with pytest.raises(RuntimeError, match="Expected a cuda device"):
-        torch.ops.image.decode_jpeg_cuda(data, ImageReadMode.UNCHANGED.value, "cpu")
+    with pytest.raises(RuntimeError, match="Expected the device parameter to be a cuda device"):
+        torch.ops.image.decode_jpegs_cuda([data], ImageReadMode.UNCHANGED.value, "cpu")
+    with pytest.raises(ValueError, match="Input tensor must be a CPU tensor"):
+        decode_jpeg(
+            torch.empty((100,), dtype=torch.uint8, device="cuda"),
+        )
+    with pytest.raises(ValueError, match="Input list must contain tensors on CPU"):
+        decode_jpeg(
+            [
+                torch.empty((100,), dtype=torch.uint8, device="cuda"),
+                torch.empty((100,), dtype=torch.uint8, device="cuda"),
+            ]
+        )
+
+    with pytest.raises(ValueError, match="Input list must contain tensors on CPU"):
+        decode_jpeg(
+            [
+                torch.empty((100,), dtype=torch.uint8, device="cuda"),
+                torch.empty((100,), dtype=torch.uint8, device="cuda"),
+            ],
+            device="cuda",
+        )
+
+    with pytest.raises(ValueError, match="Input list must contain tensors on CPU"):
+        decode_jpeg(
+            [
+                torch.empty((100,), dtype=torch.uint8, device="cpu"),
+                torch.empty((100,), dtype=torch.uint8, device="cuda"),
+            ],
+            device="cuda",
+        )
+
+    with pytest.raises(RuntimeError, match="Expected a torch.uint8 tensor"):
+        decode_jpeg(
+            [
+                torch.empty((100,), dtype=torch.uint8),
+                torch.empty((100,), dtype=torch.float32),
+            ],
+            device="cuda",
+        )
+
+    with pytest.raises(RuntimeError, match="Expected a non empty 1-dimensional tensor"):
+        decode_jpeg(
+            [
+                torch.empty((100,), dtype=torch.uint8),
+                torch.empty((1, 100), dtype=torch.uint8),
+            ],
+            device="cuda",
+        )
+
+    with pytest.raises(RuntimeError, match="Error while decoding JPEG images"):
+        decode_jpeg(
+            [
+                torch.empty((100,), dtype=torch.uint8),
+                torch.empty((100,), dtype=torch.uint8),
+            ],
+            device="cuda",
+        )
+
+    with pytest.raises(ValueError, match="Input list must contain at least one element"):
+        decode_jpeg([], device="cuda")
 
 
 def test_encode_jpeg_errors():
@@ -515,12 +594,10 @@ def test_encode_jpeg_cuda_device_param():
     devices = ["cuda", torch.device("cuda")] + [torch.device(f"cuda:{i}") for i in range(num_devices)]
     results = []
     for device in devices:
-        print(f"python: device: {device}")
         results.append(encode_jpeg(data.to(device=device)))
     assert len(results) == len(devices)
     for result in results:
         assert torch.all(result.cpu() == results[0].cpu())
-
     assert current_device == torch.cuda.current_device()
     assert current_stream == torch.cuda.current_stream()