Add GIF decoder

.github/scripts/unittest.sh

@@ -9,7 +9,7 @@ eval "$($(which conda) shell.bash hook)" && conda deactivate && conda activate c
 
 echo '::group::Install testing utilities'
 # TODO: remove the <8 constraint on pytest when https://github.com/pytorch/vision/issues/8238 is closed
-pip install --progress-bar=off "pytest<8" pytest-mock pytest-cov expecttest!=0.2.0
+pip install --progress-bar=off "pytest<8" pytest-mock pytest-cov expecttest!=0.2.0 requests
 echo '::endgroup::'
 
 python test/smoke_test.py

.github/workflows/lint.yml

@@ -63,7 +63,7 @@ jobs:
 
         echo '::group::Lint C source'
         set +e
-        ./.github/scripts/run-clang-format.py -r torchvision/csrc --clang-format-executable ./clang-format
+        ./.github/scripts/run-clang-format.py -r torchvision/csrc --clang-format-executable ./clang-format --exclude "torchvision/csrc/io/image/cpu/giflib/*"
 
         if [ $? -ne 0 ]; then
           git --no-pager diff

CMakeLists.txt

@@ -80,7 +80,7 @@ include(GNUInstallDirs)
 include(CMakePackageConfigHelpers)
 
 set(TVCPP torchvision/csrc)
-list(APPEND ALLOW_LISTED ${TVCPP} ${TVCPP}/io/image ${TVCPP}/io/image/cpu ${TVCPP}/models ${TVCPP}/ops
+list(APPEND ALLOW_LISTED ${TVCPP} ${TVCPP}/io/image ${TVCPP}/io/image/cpu ${TVCPP}/io/image/cpu/giflib ${TVCPP}/models ${TVCPP}/ops
   ${TVCPP}/ops/autograd ${TVCPP}/ops/cpu ${TVCPP}/io/image/cuda)
 if(WITH_CUDA)
     list(APPEND ALLOW_LISTED ${TVCPP}/ops/cuda ${TVCPP}/ops/autocast)

CONTRIBUTING.md

@@ -74,7 +74,7 @@ We don't officially support building from source using `pip`, but _if_ you do, y
 #### Other development dependencies (some of these are needed to run tests):
 
 ```
-pip install expecttest flake8 typing mypy pytest pytest-mock scipy
+pip install expecttest flake8 typing mypy pytest pytest-mock scipy requests
 ```
 
 ## Development Process

docs/source/io.rst

@@ -19,6 +19,7 @@ Images
     encode_jpeg
     decode_jpeg
     write_jpeg
+    decode_gif
     encode_png
     decode_png
     write_png

setup.py

@@ -332,7 +332,11 @@ def get_extensions():
     image_macros += [("NVJPEG_FOUND", str(int(use_nvjpeg)))]
 
     image_path = os.path.join(extensions_dir, "io", "image")
-    image_src = glob.glob(os.path.join(image_path, "*.cpp")) + glob.glob(os.path.join(image_path, "cpu", "*.cpp"))
+    image_src = (
+        glob.glob(os.path.join(image_path, "*.cpp"))
+        + glob.glob(os.path.join(image_path, "cpu", "*.cpp"))
+        + glob.glob(os.path.join(image_path, "cpu", "giflib", "*.c"))
+    )
 
     if is_rocm_pytorch:
         image_src += glob.glob(os.path.join(image_path, "hip", "*.cpp"))
@@ -341,18 +345,17 @@ def get_extensions():
     else:
         image_src += glob.glob(os.path.join(image_path, "cuda", "*.cpp"))
 
-    if use_png or use_jpeg:
-        ext_modules.append(
-            extension(
-                "torchvision.image",
-                image_src,
-                include_dirs=image_include + include_dirs + [image_path],
-                library_dirs=image_library + library_dirs,
-                define_macros=image_macros,
-                libraries=image_link_flags,
-                extra_compile_args=extra_compile_args,
-            )
+    ext_modules.append(
+        extension(
+            "torchvision.image",
+            image_src,
+            include_dirs=image_include + include_dirs + [image_path],
+            library_dirs=image_library + library_dirs,
+            define_macros=image_macros,
+            libraries=image_link_flags,
+            extra_compile_args=extra_compile_args,
         )
+    )
 
     # Locating ffmpeg
     ffmpeg_exe = shutil.which("ffmpeg")

test/test_image.py

@@ -6,10 +6,11 @@
 
 import numpy as np
 import pytest
+import requests
 import torch
 import torchvision.transforms.functional as F
 from common_utils import assert_equal, needs_cuda
-from PIL import __version__ as PILLOW_VERSION, Image, ImageOps
+from PIL import __version__ as PILLOW_VERSION, Image, ImageOps, ImageSequence
 from torchvision.io.image import (
     _read_png_16,
     decode_image,
@@ -548,5 +549,34 @@ def test_pathlib_support(tmpdir):
     write_png(img, write_path)
 
 
+@pytest.mark.parametrize("name", ("gifgrid", "fire", "porsche", "treescap", "treescap-interlaced", "solid2", "x-trans"))
+def test_decode_gif(tmpdir, name):
+    # Using test images from GIFLIB
+    # https://sourceforge.net/p/giflib/code/ci/master/tree/pic/, we assert PIL
+    # and torchvision decoded outputs are equal.
+    # We're not testing against "welcome2" because PIL and GIFLIB disagee on what
+    # the background color should be (likely a difference in the way they handle
+    # transparency?)
+
+    path = tmpdir / f"{name}.gif"
+    url = f"https://sourceforge.net/p/giflib/code/ci/master/tree/pic/{name}.gif?format=raw"
+    with open(path, "wb") as f:
+        f.write(requests.get(url).content)
+
+    tv_out = read_image(path)
+    if tv_out.ndim == 3:
+        tv_out = tv_out[None]
+
+    assert tv_out.is_contiguous(memory_format=torch.channels_last)
+
+    # For some reason, not using Image.open() as a CM causes "ResourceWarning: unclosed file"
+    with Image.open(path) as pil_img:
+        pil_seq = ImageSequence.Iterator(pil_img)
+
+        for pil_frame, tv_frame in zip(pil_seq, tv_out):
+            pil_frame = F.pil_to_tensor(pil_frame.convert("RGB"))
+            torch.testing.assert_close(tv_frame, pil_frame, atol=0, rtol=0)
+
+
 if __name__ == "__main__":
     pytest.main([__file__])

torchvision/csrc/io/image/cpu/decode_gif.cpp

@@ -0,0 +1,141 @@
+#include "decode_gif.h"
+#include "giflib/gif_lib.h"
+
+namespace vision {
+namespace image {
+
+typedef struct reader_helper_t {
+  uint8_t const* encoded_data; // input tensor data pointer
+  size_t encoded_data_size; // size of input tensor in bytes
+  int num_bytes_read; // number of bytes read so far in the tensor
+} reader_helper_t;
+
+// That function is used by GIFLIB routines to read the encoded bytes.
+// This reads `len` bytes and writes them into `buf`. The data is read from the
+// input tensor passed to decode_gif() starting at the `num_bytes_read`
+// position.
+int read_from_tensor(GifFileType* gifFile, GifByteType* buf, int len) {
+  // the UserData field was set in DGifOpen()
+  reader_helper_t* reader_helper = (reader_helper_t*)gifFile->UserData;
+
+  auto i = 0;
+  auto num_bytes_to_read = std::min(
+      len,
+      (int)(reader_helper->encoded_data_size - reader_helper->num_bytes_read));
+  while (i < num_bytes_to_read) {
+    buf[i] = reader_helper->encoded_data[reader_helper->num_bytes_read + i];
+    i++;
+  }
+  reader_helper->num_bytes_read += i;
+  return i;
+}
+
+torch::Tensor decode_gif(const torch::Tensor& encoded_data) {
+  // LibGif docs: https://giflib.sourceforge.net/intro.html
+  // Refer over there for more details on the libgif API, API ref, and a
+  // detailed description of the GIF format.
+
+  int error = D_GIF_SUCCEEDED;
+
+  // We're using DGidOpen. The other entrypoints of libgif are
+  // DGifOpenFileName and DGifOpenFileHandle but we don't want to use those,
+  // since we need to read the encoded bytes from a tensor of encoded bytes, not
+  // from a file (for consistency with existing jpeg and png decoders). Using
+  // DGifOpen is the only way to read from a custom source.
+  // For that we need to provide a reader function `read_from_tensor` that
+  // reads from the tensor, and we have to keep track of the number of bytes
+  // read so far: this is why we need the reader_helper struct.
+
+  // TODO: We are potentially doing an unnecessary copy of the encoded bytes:
+  // - 1 copy in from file to tensor (in read_file())
+  // - 1 copy from tensor to GIFLIB buffers (in read_from_tensor())
+  // Since we're vendoring GIFLIB we can potentially modify the calls to
+  // InternalRead() and just set the `buf` pointer to the tensor data directly.
+  // That might even save allocation of those buffers.
+  // If we do that, we'd have to make sure the buffers are never written to by
+  // GIFLIB, otherwise we'd be overridding the tensor data.
+  reader_helper_t reader_helper;
+  reader_helper.encoded_data = encoded_data.data_ptr<uint8_t>();
+  reader_helper.encoded_data_size = encoded_data.numel();
+  reader_helper.num_bytes_read = 0;
+  GifFileType* gifFile =
+      DGifOpen((void*)&reader_helper, read_from_tensor, &error);
+
+  TORCH_CHECK(
+      (gifFile != nullptr) && (error == D_GIF_SUCCEEDED),
+      "DGifOpenFileName() failed - ",
+      error);
+
+  if (DGifSlurp(gifFile) == GIF_ERROR) {
+    auto gifFileError = gifFile->Error;
+    DGifCloseFile(gifFile, &error);
+    TORCH_CHECK(false, "DGifSlurp() failed - ", gifFileError);
+  }
+
+  // Note:
+  // The GIF format has this notion of "canvas" and "canvas size", where each
+  // image could be displayed on the canvas at different offsets, forming a
+  // mosaic/picture wall like so:
+  //
+  // <---    canvas W    --->
+  // ------------------------     ^
+  // |         |            |     |
+  // |   img1  |    img3    |     |
+  // |         |------------|  canvas H
+  // |----------            |     |
+  // |   img2  |    img4    |     |
+  // |         |            |     |
+  // ------------------------     v
+  // The GifLib docs indicate that this is mostly vestigial, and modern viewers
+  // ignore the canvas size as well as image offsets. Hence, we're ignoring that
+  // too:
+  // - We're ignoring the canvas width and height and assume that the shape of
+  // the canvas and of all images is the shape of the first image.
+  // - We're enforcing that all images have the same shape.
+  // - Left and Top offsets of each image are ignored as well and assumed to be
+  // 0.
+
+  auto out_h = gifFile->SavedImages[0].ImageDesc.Height;
+  auto out_w = gifFile->SavedImages[0].ImageDesc.Width;
+  auto num_images = gifFile->ImageCount;
+
+  auto options = torch::TensorOptions()
+                     .dtype(torch::kU8)
+                     .memory_format(torch::MemoryFormat::ChannelsLast);
+  auto out = torch::empty(
+      {int64_t(num_images), 3, int64_t(out_h), int64_t(out_w)}, options);
+  auto out_a = out.accessor<uint8_t, 4>();
+
+  for (int i = 0; i < num_images; i++) {
+    const SavedImage& img = gifFile->SavedImages[i];
+    const GifImageDesc& desc = img.ImageDesc;
+    TORCH_CHECK(
+        desc.Width == out_w && desc.Height == out_h,
+        "All images in the gif should have the same dimensions.");
+
+    const ColorMapObject* cmap =
+        desc.ColorMap ? desc.ColorMap : gifFile->SColorMap;
+    TORCH_CHECK(
+        cmap != nullptr,
+        "Global and local color maps are missing. This should never happen!");
+
+    for (int h = 0; h < desc.Height; h++) {
+      for (int w = 0; w < desc.Width; w++) {
+        auto c = img.RasterBits[h * desc.Width + w];
+        GifColorType rgb = cmap->Colors[c];
+        out_a[i][0][h][w] = rgb.Red;
+        out_a[i][1][h][w] = rgb.Green;
+        out_a[i][2][h][w] = rgb.Blue;
+      }
+    }
+  }
+  out = out.squeeze(0); // remove batch dim if there's only one image
+
+  DGifCloseFile(gifFile, &error);
+  TORCH_CHECK(error == D_GIF_SUCCEEDED, "DGifCloseFile() failed - ", error);
+
+  return out;
+}
+
+} // namespace image
+} // namespace vision

torchvision/csrc/io/image/cpu/decode_gif.h

@@ -0,0 +1,11 @@
+#pragma once
+
+#include <torch/types.h>
+
+namespace vision {
+namespace image {
+
+C10_EXPORT torch::Tensor decode_gif(const torch::Tensor& encoded_data);
+
+} // namespace image
+} // namespace vision

torchvision/csrc/io/image/cpu/decode_image.cpp

@@ -1,5 +1,6 @@
 #include "decode_image.h"
 
+#include "decode_gif.h"
 #include "decode_jpeg.h"
 #include "decode_png.h"
 
@@ -23,16 +24,24 @@ torch::Tensor decode_image(
 
   const uint8_t jpeg_signature[3] = {255, 216, 255}; // == "\xFF\xD8\xFF"
   const uint8_t png_signature[4] = {137, 80, 78, 71}; // == "\211PNG"
+  const uint8_t gif_signature_1[6] = {
+      0x47, 0x49, 0x46, 0x38, 0x39, 0x61}; // == "GIF89a"
+  const uint8_t gif_signature_2[6] = {
+      0x47, 0x49, 0x46, 0x38, 0x37, 0x61}; // == "GIF87a"
 
   if (memcmp(jpeg_signature, datap, 3) == 0) {
     return decode_jpeg(data, mode, apply_exif_orientation);
   } else if (memcmp(png_signature, datap, 4) == 0) {
     return decode_png(
         data, mode, /*allow_16_bits=*/false, apply_exif_orientation);
+  } else if (
+      memcmp(gif_signature_1, datap, 6) == 0 ||
+      memcmp(gif_signature_2, datap, 6) == 0) {
+    return decode_gif(data);
   } else {
     TORCH_CHECK(
         false,
-        "Unsupported image file. Only jpeg and png ",
+        "Unsupported image file. Only jpeg, png and gif ",
         "are currently supported.");
   }
 }

torchvision/csrc/io/image/cpu/giflib/README

@@ -0,0 +1,28 @@
+These files come from the GIFLIB project (https://giflib.sourceforge.net/) and
+are licensed under the MIT license.
+
+Some modifications have been made to the original files:
+- Remove use of "register" keyword in gifalloc.c for C++17 compatibility.
+- Declare loop variable i in DGifGetImageHeader as int instead of unsigned int.
+
+Below is the original license text from the COPYING file of the GIFLIB project:
+
+= MIT LICENSE
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.