Adding GPU acceleration to encode_jpeg

benchmarks/encoding.py

@@ -0,0 +1,67 @@
+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_benchmark(batch):
+    results = []
+    for device in ["cpu", "cuda"]:
+        batch_device = [t.to(device=device) for t in batch]
+        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 batch_input]",
+                        "torchvision.io.encode_jpeg(batch_input)",
+                    ],
+                    ["unfused", "fused"],
+                ):
+                    batch_input = batch_device[:size]
+                    t = benchmark.Timer(
+                        stmt=stmt,
+                        setup="import torchvision",
+                        globals={"batch_input": batch_input},
+                        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()
+
+
+if __name__ == "__main__":
+    print_machine_specs()
+    batch = get_data()
+    mean_h, mean_w = statistics.mean(t.shape[-2] for t in batch), statistics.mean(t.shape[-1] for t in batch)
+    print(f"\nMean image size: {int(mean_h)}x{int(mean_w)}")
+    run_benchmark(batch)

test/test_image.py

@@ -1,3 +1,4 @@
+import concurrent.futures
 import glob
 import io
 import os
@@ -10,7 +11,7 @@
 import requests
 import torch
 import torchvision.transforms.functional as F
-from common_utils import assert_equal, IN_OSS_CI, needs_cuda
+from common_utils import assert_equal, cpu_and_cuda, IN_OSS_CI, needs_cuda
 from PIL import __version__ as PILLOW_VERSION, Image, ImageOps, ImageSequence
 from torchvision.io.image import (
     _read_png_16,
@@ -508,6 +509,200 @@ def test_encode_jpeg(img_path, scripted):
         assert_equal(encoded_jpeg_torch, encoded_jpeg_pil)
 
 
+@needs_cuda
+def test_encode_jpeg_cuda_device_param():
+    path = next(path for path in get_images(IMAGE_ROOT, ".jpg") if "cmyk" not in path)
+
+    data = read_image(path)
+
+    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:
+        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()
+
+
+@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("scripted", (False, True))
+@pytest.mark.parametrize("contiguous", (False, True))
+def test_encode_jpeg_cuda(img_path, scripted, contiguous):
+    decoded_image_tv = read_image(img_path)
+    encode_fn = torch.jit.script(encode_jpeg) if scripted else encode_jpeg
+
+    if "cmyk" in img_path:
+        pytest.xfail("Encoding a CMYK jpeg isn't supported")
+    if decoded_image_tv.shape[0] == 1:
+        pytest.xfail("Decoding a grayscale jpeg isn't supported")
+        # For more detail as to why check out: https://github.com/NVIDIA/cuda-samples/issues/23#issuecomment-559283013
+    if contiguous:
+        decoded_image_tv = decoded_image_tv[None].contiguous(memory_format=torch.contiguous_format)[0]
+    else:
+        decoded_image_tv = decoded_image_tv[None].contiguous(memory_format=torch.channels_last)[0]
+    encoded_jpeg_cuda_tv = encode_fn(decoded_image_tv.cuda(), quality=75)
+    decoded_jpeg_cuda_tv = decode_jpeg(encoded_jpeg_cuda_tv.cpu())
+
+    # the actual encoded bytestreams from libnvjpeg and libjpeg-turbo differ for the same quality
+    # instead, we re-decode the encoded image and compare to the original
+    abs_mean_diff = (decoded_jpeg_cuda_tv.float() - decoded_image_tv.float()).abs().mean().item()
+    assert abs_mean_diff < 3
+
+
+@pytest.mark.parametrize("device", cpu_and_cuda())
+@pytest.mark.parametrize("scripted", (True, False))
+@pytest.mark.parametrize("contiguous", (True, False))
+def test_encode_jpegs_batch(scripted, contiguous, device):
+    if device == "cpu" and IS_MACOS:
+        pytest.skip("https://github.com/pytorch/vision/issues/8031")
+    decoded_images_tv = []
+    for jpeg_path in get_images(IMAGE_ROOT, ".jpg"):
+        if "cmyk" in jpeg_path:
+            continue
+        decoded_image = read_image(jpeg_path)
+        if decoded_image.shape[0] == 1:
+            continue
+        if contiguous:
+            decoded_image = decoded_image[None].contiguous(memory_format=torch.contiguous_format)[0]
+        else:
+            decoded_image = decoded_image[None].contiguous(memory_format=torch.channels_last)[0]
+        decoded_images_tv.append(decoded_image)
+
+    encode_fn = torch.jit.script(encode_jpeg) if scripted else encode_jpeg
+
+    decoded_images_tv_device = [img.to(device=device) for img in decoded_images_tv]
+    encoded_jpegs_tv_device = encode_fn(decoded_images_tv_device, quality=75)
+    encoded_jpegs_tv_device = [decode_jpeg(img.cpu()) for img in encoded_jpegs_tv_device]
+
+    for original, encoded_decoded in zip(decoded_images_tv, encoded_jpegs_tv_device):
+        c, h, w = original.shape
+        abs_mean_diff = (original.float() - encoded_decoded.float()).abs().mean().item()
+        assert abs_mean_diff < 3
+
+    # test multithreaded decoding
+    # in the current version we prevent this by using a lock but we still want to test it
+    num_workers = 10
+    with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:
+        futures = [executor.submit(encode_fn, decoded_images_tv_device) for _ in range(num_workers)]
+    encoded_images_threaded = [future.result() for future in futures]
+    assert len(encoded_images_threaded) == num_workers
+    for encoded_images in encoded_images_threaded:
+        assert len(decoded_images_tv_device) == len(encoded_images)
+        for i, (encoded_image_cuda, decoded_image_tv) in enumerate(zip(encoded_images, decoded_images_tv_device)):
+            # make sure all the threads produce identical outputs
+            assert torch.all(encoded_image_cuda == encoded_images_threaded[0][i])
+
+            # make sure the outputs are identical or close enough to baseline
+            decoded_cuda_encoded_image = decode_jpeg(encoded_image_cuda.cpu())
+            assert decoded_cuda_encoded_image.shape == decoded_image_tv.shape
+            assert decoded_cuda_encoded_image.dtype == decoded_image_tv.dtype
+            assert (decoded_cuda_encoded_image.cpu().float() - decoded_image_tv.cpu().float()).abs().mean() < 3
+
+
+@needs_cuda
+def test_single_encode_jpeg_cuda_errors():
+    with pytest.raises(RuntimeError, match="Input tensor dtype should be uint8"):
+        encode_jpeg(torch.empty((3, 100, 100), dtype=torch.float32, device="cuda"))
+
+    with pytest.raises(RuntimeError, match="The number of channels should be 3, got: 5"):
+        encode_jpeg(torch.empty((5, 100, 100), dtype=torch.uint8, device="cuda"))
+
+    with pytest.raises(RuntimeError, match="The number of channels should be 3, got: 1"):
+        encode_jpeg(torch.empty((1, 100, 100), dtype=torch.uint8, device="cuda"))
+
+    with pytest.raises(RuntimeError, match="Input data should be a 3-dimensional tensor"):
+        encode_jpeg(torch.empty((1, 3, 100, 100), dtype=torch.uint8, device="cuda"))
+
+    with pytest.raises(RuntimeError, match="Input data should be a 3-dimensional tensor"):
+        encode_jpeg(torch.empty((100, 100), dtype=torch.uint8, device="cuda"))
+
+
+@needs_cuda
+def test_batch_encode_jpegs_cuda_errors():
+    with pytest.raises(RuntimeError, match="Input tensor dtype should be uint8"):
+        encode_jpeg(
+            [
+                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
+                torch.empty((3, 100, 100), dtype=torch.float32, device="cuda"),
+            ]
+        )
+
+    with pytest.raises(RuntimeError, match="The number of channels should be 3, got: 5"):
+        encode_jpeg(
+            [
+                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
+                torch.empty((5, 100, 100), dtype=torch.uint8, device="cuda"),
+            ]
+        )
+
+    with pytest.raises(RuntimeError, match="The number of channels should be 3, got: 1"):
+        encode_jpeg(
+            [
+                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
+                torch.empty((1, 100, 100), dtype=torch.uint8, device="cuda"),
+            ]
+        )
+
+    with pytest.raises(RuntimeError, match="Input data should be a 3-dimensional tensor"):
+        encode_jpeg(
+            [
+                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
+                torch.empty((1, 3, 100, 100), dtype=torch.uint8, device="cuda"),
+            ]
+        )
+
+    with pytest.raises(RuntimeError, match="Input data should be a 3-dimensional tensor"):
+        encode_jpeg(
+            [
+                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
+                torch.empty((100, 100), dtype=torch.uint8, device="cuda"),
+            ]
+        )
+
+    with pytest.raises(RuntimeError, match="Input tensor should be on CPU"):
+        encode_jpeg(
+            [
+                torch.empty((3, 100, 100), dtype=torch.uint8, device="cpu"),
+                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
+            ]
+        )
+
+    with pytest.raises(
+        RuntimeError, match="All input tensors must be on the same CUDA device when encoding with nvjpeg"
+    ):
+        encode_jpeg(
+            [
+                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
+                torch.empty((3, 100, 100), dtype=torch.uint8, device="cpu"),
+            ]
+        )
+
+    if torch.cuda.device_count() >= 2:
+        with pytest.raises(
+            RuntimeError, match="All input tensors must be on the same CUDA device when encoding with nvjpeg"
+        ):
+            encode_jpeg(
+                [
+                    torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda:0"),
+                    torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda:1"),
+                ]
+            )
+
+    with pytest.raises(ValueError, match="encode_jpeg requires at least one input tensor when a list is passed"):
+        encode_jpeg([])
+
+
 @pytest.mark.skipif(IS_MACOS, reason="https://github.com/pytorch/vision/issues/8031")
 @pytest.mark.parametrize(
     "img_path",

torchvision/csrc/io/image/cuda/decode_jpeg_cuda.cpp

@@ -1,4 +1,4 @@
-#include "decode_jpeg_cuda.h"
+#include "encode_decode_jpegs_cuda.h"
 
 #include <ATen/ATen.h>
 

torchvision/csrc/io/image/cuda/decode_jpeg_cuda.h → torchvision/csrc/io/image/cuda/encode_decode_jpegs_cuda.h

@@ -2,6 +2,7 @@
 
 #include <torch/types.h>
 #include "../image_read_mode.h"
+#include "encode_jpegs_cuda.h"
 
 namespace vision {
 namespace image {
@@ -11,5 +12,9 @@ C10_EXPORT torch::Tensor decode_jpeg_cuda(
     ImageReadMode mode,
     torch::Device device);
 
+C10_EXPORT std::vector<torch::Tensor> encode_jpegs_cuda(
+    const std::vector<torch::Tensor>& decoded_images,
+    const int64_t quality);
+
 } // namespace image
 } // namespace vision