22 Jun 18:20

seemethere

fe36f06

v0.6.1

Highlights

Bump pinned PyTorch version to v1.5.1

Assets 2

21 Apr 14:31

fmassa

v0.6.0

b68adcf

Drop Python 2 support, several improvements and bugfixes

This release is the first one that officially drops support for Python 2.
It contains a number of improvements and bugfixes.

Highlights

Faster/Mask/Keypoint RCNN supports negative samples

It is now possible to feed training images to Faster / Mask / Keypoint R-CNN that do not contain any positive annotations.
This enables increasing the number of negative samples during training. For those images, the annotations expect a tensor with 0 in the number of objects dimension, as follows:

target = {"boxes": torch.zeros((0, 4), dtype=torch.float32),
          "labels": torch.zeros(0, dtype=torch.int64),
          "image_id": 4,
          "area": torch.zeros(0, dtype=torch.float32),
          "masks": torch.zeros((0, image_height, image_width), dtype=torch.uint8),
          "keypoints": torch.zeros((17, 0, 3), dtype=torch.float32),
          "iscrowd": torch.zeros((0,), dtype=torch.int64)}

Aligned flag for RoIAlign

RoIAlign now supports the aligned flag, which aligns more precisely two neighboring pixel indices.

Refactored abstractions for C++ video decoder

This change is transparent to Python users, but the whole C++ backend for video reading (which needs torchvision to be compiled from source for it to be enabled for now) has been refactored into more modular abstractions.
The core abstractions are in https://github.com/pytorch/vision/tree/master/torchvision/csrc/cpu/decoder, and the video reader functions exposed to Python, by leveraging those abstractions, can be written in a much more concise way

Backwards Incompatible Changes

Dropping Python2 support (#1761, #1792, #1984, #1976, #2037, #2033, #2017)
[Models] Fix inception quantized pre-trained model (#1954, #1969, #1975)
ONNX support for Mask R-CNN and Keypoint R-CNN has been temporarily dropped, but will be fixed in next releases

New Features

[Transforms] Add Perspective fill option (#1973)
[Ops] aligned flag in ROIAlign (#1908)
[IO] Update video reader to use new decoder (#1978)
[IO] torchscriptable functions for video io (#1653, #1794)
[Models] Support negative samples in Faster R-CNN, Mask R-CNN and Keypoint R-CNN (#1911, #2069)

Improvements

Datasets

STL10: don't check integrity twice when download=True (#1787)
Improve code readability and docstring of video datasets(#2020)
[DOC] Fixed typo in Cityscapes docs (#1851)

Transforms

Allow passing list to the input argument 'scale' of RandomResizedCrop (#1997) (#2008)
F.normalize unsqueeze mean & std only for 1-d arrays (#2002)
Improved error messages for transforms.functional.normalize(). (#1915)
generalize number of bands calculation in to_tensor (#1781)
Replace 2 transpose ops with 1 permute in ToTensor(#2018)
Fixed Pillow version check for Pillow >= 10 (#2039)
[DOC]: Improve transforms.Normalize docs (#1784, #1858)
[DOC] Fixed missing new line in transforms.Crop docstring (#1922)

Ops

Check boxes shape in RoIPool / Align (#1968)
[ONNX] Export new_empty_tensor (#1733)
Fix Tensor::data<> deprecation. (#2028)
Fix deprecation warnings (#2055)

Models

Add warning and note docs for scipy (#1842) (#1966)
Added repr attribute to GeneralizedRCNNTransform (#1834)
Replace mean on dimensions 2,3 by adaptive_avg_pooling2d in mobilenet (#1838)
Add init_weights keyword argument to Inception3 (#1832)
Add device to torch.tensor. (#1979)
ONNX export for variable input sizes in Faster R-CNN (#1840)
[JIT] Cleanup torchscript constant annotations (#1721, #1923, #1907, #1727)
[JIT] use // now that it is supported (#1658)
[JIT] add @torch.jit.script to ImageList (#1919)
[DOC] Improved docs for Faster R-CNN (#1886, #1868, #1768, #1763)
[DOC] add comments for the modified implementation of ResNet (#1983)
[DOC] Add comments to AnchorGenerator (#1941)
[DOC] Add comment in GoogleNet (#1932)

Documentation

Document int8 quantization model (#1951)
Update Doc with ONNX support (#1752)
Update README to reflect strict dependency on torch==1.4.0 (#1767)
Update sphinx theme (#2031)
Document origin of preprocessing mean / std (#1965)
Fix docstring formatting issues (#2049)

Reference scripts

Add return statement in evaluate function of detection reference script (#2029)
[DOC]Add default training parameters to classification reference README (#1998)
[DOC] Add README to references/segmentation (#1864)

Tests

Improve stability of test_nms_cuda (#2044)
[ONNX] Disable model tests since export of interpolate script module is broken (#1989)
Skip inception v3 in test/test_quantized_models (#1885)
[LINT] Small indentation fix (#1831)

Misc

Remove unintentional -O0 option in setup.py (#1770)
Create CODE_OF_CONDUCT.md
Update issue templates (#1913, #1914)
master version bump 0.5 → 0.6
replace torch 1.5.0 items flagged with deprecation warnings (fix #1906) (#1918)
CUDA_SUFFIX → PYTORCH_VERSION_SUFFIX

CI

Remove av from the binary requirements (#2006)
ci: Add cu102 to CI and packaging, remove cu100 (#1980)
.circleci: Switch to use token for conda uploads (#1960)
Improvements to CI infra (#2051, #2032, #2046, #1735, #2048, #1789, #1731, #1961)
typing only needed for python 3.5 and previous (#1778)
Move C++ and Python linter to CircleCI (#2056, #2057)

Bug Fixes

Datasets

bug fix on downloading voc2007 test dataset (#1991)
fix lsun docstring example (#1935)
Fixes EMNIST classes attribute is wrong #1716 (#1736)
Force object annotation to be a list in VOC (#1790)

Models

Fix for AnchorGenerator when device switch happen (#1745)
[JIT] fix len error (#1981)
[JIT] fix googlenet no aux logits (#1949)
[JIT] Fix quantized googlenet (#1974)

Transforms

Fix for rotate fill with Images of type F (#1828)
Fix fill in rotate (#1760)

Ops

Fix bug in DeformConv2d for batch sizes > 32 (#2027, #2040)
Fix for roi_align ONNX export (#1988)
Fix torchscript issue in ConvTranspose2d (#1917)
Fix interpolate when no scale_factor is passed (#1785)
Fix Windows build by renaming Python init functions (#1779)
fix for loading models with num_batches_tracked in frozen bn (#1728)

Deprecations

the pts_unit of pts from read_video and read_video_timestamp is deprecated, and will be replaced in next releases with seconds.

Assets 2

15 Jan 22:11

fmassa

v0.5.0

85b8fbf

Towards better research to production support

This release brings several new additions to torchvision that improves support for deployment. Most notably, all models in torchvision are torchscript-compatible, and can be exported to ONNX. Additionally, a few classification models have quantized weights.

Note: this is the last version of torchvision that officially supports Python 2.

Breaking changes

Updated KeypointRCNN pre-trained weights

The pre-trained weights for keypointrcnn_resnet50_fpn have been updated and now correspond to the results reported in the documentation. The previous weights corresponded to an intermediate training checkpoint. (#1609)

Corrected the implementation for MNASNet

The previous implementation contained a bug which affects all MNASNet variants other than mnasnet1_0. The bug was that the first few layers needed to also be scaled in terms of width multiplier, along with all the rest. We now provide a new checkpoint for mnasnet0_5, which gives 32.17 top1 error. (#1224)

Highlights

TorchScript support for all models

All models in torchvision have native support for torchscript, for both training and testing. This includes complex models such as DeepLabV3, Mask R-CNN and Keypoint R-CNN.
Using torchscript with torchvision models is easy:

# get a pre-trained model
model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)

# convert to torchscript
model_script = torch.jit.script(model)
model_script.eval()

# compute predictions
predictions = model_script([torch.rand(3, 300, 300)])

Warning: the return type for the scripted version of Faster R-CNN, Mask R-CNN and Keypoint R-CNN is different from its eager counterpart, and it always returns a tuple of losses, detections. This discrepancy will be addressed in a future release.

ONNX

All models in torchvision can now be exported to ONNX for deployment. This includes models such as Mask R-CNN.

# get a pre-trained model
model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)
model.eval()
inputs = [torch.rand(3, 300, 300)]
predictions = model(inputs)

# convert to ONNX
torch.onnx.export(model, inputs, "model.onnx",
                  do_constant_folding=True,
                  opset_version=11  # opset_version 11 required for Mask R-CNN
                  )

Warning: for Faster R-CNN / Mask R-CNN / Keypoint R-CNN, the current exported model is dependent on the input shape during export. As such, make sure that once the model has been exported to ONNX that all images that are fed to it have the same shape as the shape used to export the model to ONNX. This behavior will be made more general in a future release.

Quantized models

torchvision now provides quantized models for ResNet, ResNext, MobileNetV2, GoogleNet, InceptionV3 and ShuffleNetV2, as well as reference scripts for quantizing your own model in references/classification/train_quantization.py (https://github.com/pytorch/vision/blob/master/references/classification/train_quantization.py). Obtaining a pre-trained quantized model can be obtained with a few lines of code:

model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
model.eval()

# run the model with quantized inputs and weights
out = model(torch.rand(1, 3, 224, 224))

We provide pre-trained quantized weights for the following models:

Model	Acc@1	Acc@5
MobileNet V2	71.658	90.150
ShuffleNet V2:	68.360	87.582
ResNet 18	69.494	88.882
ResNet 50	75.920	92.814
ResNext 101 32x8d	78.986	94.480
Inception V3	77.084	93.398
GoogleNet	69.826	89.404

Torchscript support for torchvision.ops

torchvision ops are now natively supported by torchscript. This includes operators such as nms, roi_align and roi_pool, and for the ops that support backpropagation, both eager and torchscript modes are supported in autograd.

New operators

Deformable Convolution (#1586) (#1660) (#1637)

As described in Deformable Convolutional Networks (https://arxiv.org/abs/1703.06211), torchvision now supports deformable convolutions. The model expects as input both the input as well as the offsets, and can be used as follows:

from torchvision import ops

module = ops.DeformConv2d(in_channels=1, out_channels=1, kernel_size=3, padding=1)
x = torch.rand(1, 1, 10, 10)

# number of channels for offset should be a multiple
# of 2 * module.weight.size[2] * module.weight.size[3], which correspond
# to the kernel_size
offset = torch.rand(1, 2 * 3 * 3, 10, 10)

# the output requires both the input and the offsets
out = module(x, offset)

If needed, the user can create their own wrapper module that imposes constraints on the offset. Here is an example, using a single convolution layer to compute the offset:

class BasicDeformConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
                 dilation=1, groups=1, offset_groups=1):
        super().__init__()
        offset_channels = 2 * kernel_size * kernel_size
        self.conv2d_offset = nn.Conv2d(
            in_channels,
            offset_channels * offset_groups,
            kernel_size=3,
            stride=stride,
            padding=dilation,
            dilation=dilation,
        )
        self.conv2d = ops.DeformConv2d(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=dilation,
            dilation=dilation,
            groups=groups,
            bias=False
        )
    
    def forward(self, x):
        offset = self.conv2d_offset(x)
        return self.conv2d(x, offset)

Position-sensitive RoI Pool / Align (#1410)

Position-Sensitive Region of Interest (RoI) Align operator mentioned in Light-Head R-CNN (https://arxiv.org/abs/1711.07264). These are available under ops.ps_roi_align, ps_roi_pool and the module equivalents ops.PSRoIAlign and ops.PSRoIPool, and have the same interface as RoIAlign / RoIPool.

New Features

TorchScript support

Bugfix in BalancedPositiveNegativeSampler introduced during torchscript support (#1670)
Make R-CNN models less verbose in script mode (#1671)
Minor torchscript fixes for Mask R-CNN (#1639)
remove BC-breaking changes (#1560)
Make maskrcnn scriptable (#1407)
Add Script Support for Video Resnet Models (#1393)
fix ASPPPooling (#1575)
Test that torchhub models are scriptable (#1242)
Make Googlnet & InceptionNet scriptable (#1349)
Make fcn_resnet Scriptable (#1352)
Make Densenet Scriptable (#1342)
make resnext scriptable (#1343)
make shufflenet and resnet scriptable (#1270)

ONNX

Enable KeypointRCNN test (#1673)
enable mask rcnn test (#1613)
Changes to Enable KeypointRCNN ONNX Export (#1593)
Disable Profiling in Failing Test (#1585)
Enable ONNX Test for FasterRcnn (#1555)
Support Exporting Mask Rcnn to ONNX (#1461)
Lahaidar/export faster rcnn (#1401)
Support Exporting RPN to ONNX (#1329)
Support Exporting MultiScaleRoiAlign to ONNX (#1324)
Support Exporting GeneralizedRCNNTransform to ONNX (#1325)

Quantization

Update quantized shufflenet weights (#1715)
Add commands to run quantized model with pretrained weights (#1547)
Quantizable googlenet, inceptionv3 and shufflenetv2 models (#1503)
Quantizable resnet and mobilenet models (#1471)
Remove model download from test_quantized_models (#1526)

Improvements

Bugfixes

Bugfix on GroupedBatchSampler for corner case where there are not enough examples in a category to form a batch (#1677)
Fix rpn memory leak and dataType errors. (#1657)
Fix torchvision install due to zippeg egg (#1536)

Transforms

Make shear operation area preserving (#1529)
PILLOW_VERSION deprecation updates (#1501)
Adds optional fill colour to rotate (#1280)

Ops

Add Deformable Convolution operation. (#1586) (#1660) (#1637)
Fix inconsistent NMS implementation between CPU and CUDA (#1556)
Speed up nms_cuda (#1704)
Implementation for Position-sensitive ROI Pool/Align (#1410)
Remove cpp extensions in favor of torch ops (#1348)
Make custom ops differentiable (#1314)
Fix Windows build in Torchvision Custom op Registration (#1320)
Revert "Register Torchvision Ops as Cutom Ops (#1267)" (#1316)
Register Torchvision Ops as Cutom Ops (#1267)
Use Tensor.data_ptr instead of .data (#1262)
Fix header includes for cpu (#1644)

Datasets

fixed test for windows by closing the created temporary files (#1662)
VideoClips windows fixes (#1661)
Fix VOC on Windows (#1641)
update dead LSUN link (#1626)
DatasetFolder should follow links when searching for data (#1580)
add .tgz support to extract_archive (#1650)
expose audio_channels as a parameter to kinetics dataset (#1559)
Implemented integrity check (md5 hash) after dataset download (#1456)
Move VideoClips dummy dataset to top level for pickling (#1649)
Remove download for ImageNet (#1457)
add tar.xz archive handler (#1361)
Fix DeprecationWarning for collections.Iterable import in LSUN (#1417)
Support empty target_type for CelebA dataset (#1351)
VOC2007 support test set (#1340)
Fix EMNSIT download URL (#1297) (#1318)
Refactored clip_sampler (#1562)

Documentation

Fix documentation for NMS (#1614)
More examples of functional transforms (#1402)
Fixed doc of crop functionals (#1388)
Added Training Sample code for fasterrcnn_resnet50_fpn (#1695)
Fix rpn.py typo (#1276)
Update README with minimum required version of PyTorch (#1272)
fix alignment of README (#1396)
fixed typo in DatasetFolder and ImageFolder (#1284)

Models

Bugfix for MNASNet (#1224)
Fix anchor dtype in AnchorGenerator (#1341)

Utils

Adding File...

Assets 2

07 Nov 16:33

fmassa

v0.4.2

efb0b26

Optimized video reader backend

This minor release introduces an optimized video_reader backend for torchvision. It is implemented in C++, and uses FFmpeg internally.

The new video_reader backend can be up to 6 times faster compared to the pyav backend.

When decoding all video/audio frames in the video, the new video_reader is 1.2x - 6x faster depending on the codec and video length.
When decoding a fixed number of video frames (e.g. [4, 8, 16, 32, 64, 128]), video_reader runs equally fast for small values (i.e. [4, 8, 16]) and runs up to 3x faster for large values (e.g. [32, 64, 128]).

Using the optimized video backend

Switching to the new backend can be done via torchvision.set_video_backend('video_reader') function. By default, we use a backend based on top of PyAV.

Due to packaging issues with FFmpeg, in order to use the video_reader backend one need to first have ffmpeg available on the system, and then compile torchvision from source using the instructions from https://github.com/pytorch/vision#installation

Deprecations

In torchvision 0.4.0, the read_video and read_video_timestamps functions used pts relative to the video stream. This could lead to unaligned video-audio being returned in some cases.

torchvision now allow to specify a pts_unit argument in those functions. The default value is 'pts' (with same behavior as before), and the user can now specify pts_unit='sec', which produces consistently aligned results for both video and audio. The 'pts' value is deprecated for now, and kept for backwards-compatibility.

In the next release, the default value of pts_unit will change to 'sec', so that calling read_video without specifying pts_unit returns consistently aligned audio-video results. This will require users to update their VideoClips checkpoints, which used to store the information in pts by default.

Changelog

[video reader] inception commit (#1303) 31fad34
Expose frame-rate and cache to video datasets (#1356) 85ffd93
Expose num_workers in VideoClips (#1359) 02a8c0a
Fix randomresized params flaky (#1282) 7c9bbf5
Video transforms (#1353) 64917bc
add _backend argument to init() of class VideoClips (#1363) 7874374
Video clips workers (#1369) 0982395
modified code of io.read_video and io.read_video_timestamps to intepret pts values in seconds (#1331) 17e355f
add metadata to video dataset classes. bug fix. more robustness (#1376) 49b01e3
move sampler into TV core. Update UniformClipSampler (#1408) f0d3daa
remove hardcoded video extension in kinetics400 dataset (#1418) 929c81d
Fix hmdb51 and ucf101 typo (#1420) b13931a
fix a bug related to audio_end_pts (#1431) 1258bb7
expose more io api (#1423) e48b958
Make video transforms private (#1429) 79daca1
extend video reader to support fast video probing (#1437) ed5b2dc
Better handle corrupted videos (#1463) da89dad
Temporary fix to remove ffmpeg from build time (#1475) ed04dee
fix a bug when video decoding fails and empty frames are returned (#1506) 2804c12
extend DistributedSampler to support group_size (#1512) 355e9d2
Unify video backend (#1514) 97b53f9
Unify video metadata in VideoClips (#1527) 7d509c5
Fixed compute_clips docstring (#1543) b438d32

Assets 2

30 Oct 15:18

fmassa

v0.4.1

a263704

Compat with PyTorch 1.3 and bugfix

This minor release provides binaries compatible with PyTorch 1.3.

Compared to version 0.4.0, it contains a single bugfix for HMDB51 and UCF101 datasets, fixed in #1240

Assets 2

08 Aug 15:09

fmassa

v0.4.0

d31eafa

Video support, new datasets and models

This release adds support for video models and datasets, and brings several improvements.

Note: torchvision 0.4 requires PyTorch 1.2 or newer

Highlights

Video and IO

Video is now a first-class citizen in torchvision. The 0.4 release includes:

efficient IO primitives for reading and writing video files
Kinetics-400, HMDB51 and UCF101 datasets for action recognition, which are compatible with torch.utils.data.DataLoader
Pre-trained models for action recognition, trained on Kinetics-400
Training and evaluation scripts for reproducing the training results.

Writing your own video dataset is easy. We provide an utility class VideoClips that simplifies the task of enumerating all possible clips of fixed size in a list of video files by creating an index of all clips in a set of videos. It additionally allows to specify a fixed frame-rate for the videos.

from torchvision.datasets.video_utils import VideoClips

class MyVideoDataset(object):
    def __init__(self, video_paths):
        self.video_clips = VideoClips(video_paths,
                                      clip_length_in_frames=16,
                                      frames_between_clips``=1,
                                      frame_rate=15)

    def __getitem__(self, idx):
        video, audio, info, video_idx = self.video_clips.get_clip(idx)
        return video, audio
    
    def __len__(self):
        return self.video_clips.num_clips()

We provide pre-trained models for action recognition, trained on Kinetics-400, which reproduce the results on the original papers where they have been first introduced, as well the corresponding training scripts.

model	clip @ 1
r3d_18	52.748
mc3_18	53.898
r2plus1d_18	57.498

Bugfixes

change aspect ratio calculation formula in references/detection (#1194)
bug fixes in ImageNet (#1149)
fix save_image when height or width equals 1 (#1059)
Fix STL10 __repr__ (#969)
Fix wrong behavior of GeneralizedRCNNTransform in Python2. (#960)

Datasets

New

Add USPS dataset (#961)(#1117)
Added support for the QMNIST dataset (#995)
Add HMDB51 and UCF101 datasets (#1156)
Add Kinetics400 dataset (#1077)

Improvements

Miscellaneous dataset fixes (#1174)
Standardize str argument verification in datasets (#1167)
Always pass transform and target_transform to abstract dataset (#1126)
Remove duplicate transform assignment in FakeDataset (#1125)
Automatic extraction for Cityscapes Dataset (#1066) (#1068)
Use joint transform in Cityscapes (#1024)(#1045)
CelebA: track attr names, support split="all", code cleanup (#1008)
Add folds option to STL10 (#914)

Models

New

Add pretrained Wide ResNet (#912)
Memory efficient densenet (#1003) (#1090)
Implementation of the MNASNet family of models (#829)(#1043)(#1092)
Add VideoModelZoo models (#1130)

Improvements

Fix resnet fpn backbone for resnet18 and resnet34 (#1147)
Add checks to roi_heads in detection module (#1091)
Make shallow copy of input list in GeneralizedRCNNTransform (#1085)(#1111)(#1084)
Make MobileNetV2 number of channel divisible by 8 (#1005)
typo fix: ouput -> output in Inception and GoogleNet (#1034)
Remove empty proposals from the RPN (#1026)
Remove empty boxes before NMS (#1019)
Reduce code duplication in segmentation models (#1009)
allow user to define residual settings in MobileNetV2 (#965)
Use flatten instead of view (#1134)

Documentation

Consistency in detection box format (#1110)
Fix Mask R-CNN docs (#1089)
Add paper references to VGG and Resnet variants (#1088)
Doc, Test Fixes in Normalize (#1063)
Add transforms doc to more datasets (#1038)
Corrected typo: 5 to 0.5 (#1041)
Update doc for torchvision.transforms.functional.perspective (#1017)
Improve documentation for fillcolor option in RandomAffine (#994)
Fix COCO_INSTANCE_CATEGORY_NAMES (#991)
Added models information to documentation. (#985)
Add missing import in faster_rcnn.py documentation (#979)
Improve make_grid docs (#964)

Tests

Add test for SVHN (#1086)
Add tests for Cityscapes Dataset (#1079)
Update CI to Python 3.6 (#1044)
Make test_save_image more robust (#1037)
Add a generic test for the datasets (#1015)
moved fakedata generation to separate module (#1014)
Create imagenet fakedata on-the-fly (#1012)
Minor test refactorings (#1011)
Add test for CIFAR10(0) (#1010)
Mock MNIST download for less flaky tests (#1004)
Add test for ImageNet (#976)(#1006)
Add tests for datasets (#966)

Transforms

New

Add Random Erasing for image augmentation (#909) (#1060) (#1087) (#1095)

Improvements

Allowing 'F' mode for 1 channel FloatTensor in ToPILImage (#1100)
Add shear parallel to y-axis (#1070)
fix error message in to_tensor (#1000)
Fix TypeError in RandomResizedCrop.get_params (#1036)
Fix normalize for different dtype than float32 (#1021)

Ops

Renamed vision.h files to vision_cpu.h and vision_cuda.h (#1051)(#1052)
Optimize nms_cuda by avoiding extra torch.cat call (#945)

Reference scripts

Expose data-path in the detection reference scripts (#1109)
Make utils.py work with pytorch-cpu (#1023)
Add mixed precision training with Apex (#972)(#1124)
Add reference code for similarity learning (#1101)

Build

Add windows build steps and wheel build scripts (#998)
add packaging scripts (#996)
Allow forcing GPU build with FORCE_CUDA=1 (#927)

Misc

Misc lint fixes (#1020)
Reraise error on failed downloading (#1013)
add more hub models (#974)
make C extension lazy-import (#971)

Assets 2

22 May 19:45

fmassa

v0.3.0

be37608

Training scripts, detection/segmentation models and more

This release brings several new features to torchvision, including models for semantic segmentation, object detection, instance segmentation and person keypoint detection, and custom C++ / CUDA ops specific to computer vision.

Note: torchvision 0.3 requires PyTorch 1.1 or newer

Highlights

Reference training / evaluation scripts

We now provide under the references/ folder scripts for training and evaluation of the following tasks: classification, semantic segmentation, object detection, instance segmentation and person keypoint detection.
Their purpose is twofold:

serve as a log of how to train a specific model.
provide baseline training and evaluation scripts to bootstrap research

They all have an entry-point train.py which performs both training and evaluation for a particular task. Other helper files, specific to each training script, are also present in the folder, and they might get integrated into the torchvision library in the future.

We expect users should copy-paste and modify those reference scripts and use them for their own needs.

TorchVision Ops

TorchVision now contains custom C++ / CUDA operators in torchvision.ops. Those operators are specific to computer vision, and make it easier to build object detection models.
Those operators currently do not support PyTorch script mode, but support for it is planned for future releases.

List of supported ops

roi_pool (and the module version RoIPool)
roi_align (and the module version RoIAlign)
nms, for non-maximum suppression of bounding boxes
box_iou, for computing the intersection over union metric between two sets of bounding boxes

All the other ops present in torchvision.ops and its subfolders are experimental, in particular:

FeaturePyramidNetwork is a module that adds a FPN on top of a module that returns a set of feature maps.
MultiScaleRoIAlign is a wrapper around roi_align that works with multiple feature map scales

Here are a few examples on using torchvision ops:

import torch
import torchvision

# create 10 random boxes
boxes = torch.rand(10, 4) * 100
# they need to be in [x0, y0, x1, y1] format
boxes[:, 2:] += boxes[:, :2]
# create a random image
image = torch.rand(1, 3, 200, 200)
# extract regions in `image` defined in `boxes`, rescaling
# them to have a size of 3x3
pooled_regions = torchvision.ops.roi_align(image, [boxes], output_size=(3, 3))
# check the size
print(pooled_regions.shape)
# torch.Size([10, 3, 3, 3])

# or compute the intersection over union between
# all pairs of boxes
print(torchvision.ops.box_iou(boxes, boxes).shape)
# torch.Size([10, 10])

Models for more tasks

The 0.3 release of torchvision includes pre-trained models for other tasks than image classification on ImageNet.
We include two new categories of models: region-based models, like Faster R-CNN, and dense pixelwise prediction models, like DeepLabV3.

Object Detection, Instance Segmentation and Person Keypoint Detection models

Warning: The API is currently experimental and might change in future versions of torchvision

The 0.3 release contains pre-trained models for Faster R-CNN, Mask R-CNN and Keypoint R-CNN, all of them using ResNet-50 backbone with FPN.
They have been trained on COCO train2017 following the reference scripts in references/, and give the following results on COCO val2017

Network	box AP	mask AP	keypoint AP
Faster R-CNN ResNet-50 FPN	37.0
Mask R-CNN ResNet-50 FPN	37.9	34.6
Keypoint R-CNN ResNet-50 FPN	54.6		65.0

The implementations of the models for object detection, instance segmentation and keypoint detection are fast, specially during training.

In the following table, we use 8 V100 GPUs, with CUDA 10.0 and CUDNN 7.4 to report the results. During training, we use a batch size of 2 per GPU, and during testing a batch size of 1 is used.

For test time, we report the time for the model evaluation and post-processing (including mask pasting in image), but not the time for computing the precision-recall.

Network	train time (s / it)	test time (s / it)	memory (GB)
Faster R-CNN ResNet-50 FPN	0.2288	0.0590	5.2
Mask R-CNN ResNet-50 FPN	0.2728	0.0903	5.4
Keypoint R-CNN ResNet-50 FPN	0.3789	0.1242	6.8

You can load and use pre-trained detection and segmentation models with a few lines of code

import torchvision

model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)
# set it to evaluation mode, as the model behaves differently
# during training and during evaluation
model.eval()

image = PIL.Image.open('/path/to/an/image.jpg')
image_tensor = torchvision.transforms.functional.to_tensor(image)

# pass a list of (potentially different sized) tensors
# to the model, in 0-1 range. The model will take care of
# batching them together and normalizing
output = model([image_tensor])
# output is a list of dict, containing the postprocessed predictions

Pixelwise Semantic Segmentation models

Warning: The API is currently experimental and might change in future versions of torchvision

The 0.3 release also contains models for dense pixelwise prediction on images.
It adds FCN and DeepLabV3 segmentation models, using a ResNet50 and ResNet101 backbones.
Pre-trained weights for ResNet101 backbone are available, and have been trained on a subset of COCO train2017, which contains the same 20 categories as those from Pascal VOC.

The pre-trained models give the following results on the subset of COCO val2017 which contain the same 20 categories as those present in Pascal VOC:

Network	mean IoU	global pixelwise acc
FCN ResNet101	63.7	91.9
DeepLabV3 ResNet101	67.4	92.4

New Datasets

Add Caltech101, Caltech256, and CelebA (#775)
ImageNet dataset (#764) (#858) (#870)
Added Semantic Boundaries Dataset (#808) (#865)
Add VisionDataset as a base class for all datasets (#749) (#859) (#838) (#876) (#878)

New Models

Classification

Add GoogLeNet (Inception v1) (#678) (#821) (#828) (#816)
Add MobileNet V2 (#818) (#917)
Add ShuffleNet v2 (#849) (#886) (#889) (#892) (#916)
Add ResNeXt-50 32x4d and ResNeXt-101 32x8d (#822) (#852) (#917)

Segmentation

Fully-Convolutional Network (FCN) with ResNet 101 backbone
DeepLabV3 with ResNet 101 backbone

Detection

Faster R-CNN R-50 FPN trained on COCO train2017 (#898) (#921)
Mask R-CNN R-50 FPN trained on COCO train2017 (#898) (#921)
Keypoint R-CNN R-50 FPN trained on COCO train2017 (#898) (#921) (#922)

Breaking changes

Make CocoDataset ids deterministically ordered (#868)

New Transforms

Add bias vector to LinearTransformation (#793) (#843) (#881)
Add Random Perspective transform (#781) (#879)

Bugfixes

Fix user warning when applying normalize (#810)
Fix logic error in check_integrity (#871)

Improvements

Fixing mutation of 2d tensors in to_pil_image (#762)
Replace tensor.view with tensor.unsqueeze(0) in make_grid (#765)
Change usage of view to reshape in resnet to enable running with mkldnn (#890)
Improve normalize to work with tensors located on any device (#787)
Raise an IndexError for FakeData.__getitem__() if the index would be out of range (#780)
Aspect ratio is now sampled from a logarithmic distribution in RandomResizedCrop. (#799)
Modernize inception v3 weight initialization code (#824)
Remove duplicate code from densenet load_state_dict (#827)
Replace endswith calls in a loop with a single endswith call in DatasetFolder (#832)
Added missing dot in webp image extensions (#836)
fix inconsistent behavior for ~ expression (#850)
Minor Compressions in statements in folder.py (#874)
Minor fix to evaluation formula of PILLOW_VERSION in transforms.functional.affine (#895)
added is_valid_file parameter to DatasetFolder (#867)
Add support for joint transformations in VisionDataset (#872)
Auto calculating return dimension of squeezenet forward method (#884)
Added progress flag to model getters (#875) (#910)
Add support for other normalizations (i.e., GroupNorm) in ResNet (#813)
Add dilation option to ResNet (#866)

Testing

Add basic model testing. (#811)
Add test for num_class in test_model.py (#815)
Added test for normalize functionality in make_grid function. (#840)
Added downloaded directory not empty check in test_datasets_utils (#844)
Added test for save_image in utils (#847)
Added tests for check_md5 and check_integrity (#873)

Misc

Remove shebang in setup.py (#773)
configurable version and package names (#842)
More hub models (#851)
Update travis to use more recent GCC (#891)

Documentation

Add comments regarding downsampling layers of resnet (#794)
Remove unnecessary bullet point in InceptionV3 doc (#814)
Fix crop and resized_crop docs in functional.py (#817)
Added dimensions in the comments of googlenet (#788)
Update transform doc with random offset of padding due to pad_if_needed (#791)
Added the argument transform_input in docs of InceptionV3 (#789)
Update documentation for MNIST datasets (#778)
Fixed typo in normalize() function. (#823)
Fix typo in squeezenet (#841)
Fix typo in DenseNet comment (#857)
Typo and syntax fixes to transform docstrings (#887)

Assets 2

26 Feb 17:13

fmassa

v0.2.2

ef768ad

More datasets, transforms and bugfixes

This version introduces several improvements and fixes.

Support for arbitrary input sizes for models

It is now possible to feed larger images than 224x224 into the models in torchvision.
We added an adaptive pooling just before the classifier, which adapts the size of the feature maps before the last layer, allowing for larger input images.
Relevant PRs: #744 #747 #746 #672 #643

Bugfixes

Fix invalid argument error when using lsun method in windows (#508)
Fix FashionMNIST loading MNIST (#640)
Fix inception v3 input transform for trace & onnx (#621)

Datasets

Add support for webp and tiff images in ImageFolder #736 #724
Add K-MNIST dataset #687
Add Cityscapes dataset #695 #725 #739 #700
Add Flicker 8k and 30k datasets #674
Add VOCDetection and VOCSegmentation datasets #663
Add SBU Captioned Photo Dataset (#665)
Updated URLs for EMNIST #726
MNIST and FashionMNIST now have their own 'raw' and 'processed' folder #601
Add metadata to some datasets (#501)

Improvements

Allow RandomCrop to crop in the padded region #564
ColorJitter now supports min/max values #548
Generalize resnet to use block.extension #487
Move area calculation out of for loop in RandomResizedCrop #641
Add option to zero-init the residual branch in resnet (#498)
Improve error messages in to_pil_image #673
Added the option of converting to tensor for numpy arrays having only two dimensions in to_tensor (#686)
Optimize _find_classes in DatasetFolder via scandir in Python3 (#559)
Add padding_mode to RandomCrop (#489 #512)
Make DatasetFolder more generic (#527)
Add in-place option to normalize (#699)
Add Hamming and Box interpolations to transforms.py (#693)
Added the support of 2-channel Image modes such as 'LA' and adding a mode in 4 channel modes (#688)
Improve support for 'P' image mode in pad (#683)
Make torchvision depend on pillow-simd if already installed (#522)
Make tests run faster (#745)
Add support for non-square crops in RandomResizedCrop (#715)

Breaking changes

save_images now round to nearest integer #754

Misc

Added code coverage to travis #703
Add downloads and docs badge to README (#702)
Add progress to download_url #497 #524 #535
Replace 'residual' with 'identity' in resnet.py (#679)
Consistency changes in the models
Refactored MNIST and CIFAR to have data and target fields #578 #594
Update torchvision to newer versions of PyTorch
Relax assertion in transforms.Lambda.__init__ (#637)
Cast MNIST target to int (#605)
Change default target type of FakedDataset to long (#581)
Improve docs of functional transforms (#602)
Docstring improvements
Add is_image_file to folder_dataset (#507)
Add deprecation warning in MNIST train[test]_labels[data] (#742)
Mention TORCH_MODEL_ZOO in models documentation. (#624)
Add scipy as a dependency to setup.py (#675)
Added size information for inception v3 (#719)

Assets 2

24 Apr 18:16

soumith

v0.2.1

1d0a3b1

New datasets, transforms and fixes

This version introduces several fixes and improvements to the previous version.

Better printing of Datasets and Transforms

Add descriptions to Transform objects.

# Now T.Compose([T.RandomHorizontalFlip(), T.RandomCrop(224), T.ToTensor()]) prints
Compose(
    RandomHorizontalFlip(p=0.5)
    RandomCrop(size=(224, 224), padding=0)
    ToTensor()
)

Add descriptions to Datasets

# now torchvision.datasets.MNIST('~') prints
Dataset MNIST
    Number of datapoints: 60000
    Split: train
    Root Location: /private/home/fmassa
    Transforms (if any): None
    Target Transforms (if any): None

New transforms

Add RandomApply, RandomChoice, RandomOrder transformations #402
- RandomApply: applies a list of transformation with a probability
- RandomChoice: choose randomly a single transformation from a list
- RandomOrder: apply transformations in a random order
Add random affine transformation #411
Add reflect, symmetric and edge padding to transforms.pad #460

Performance improvements

Speedup MNIST preprocessing by a factor of 1000x
make weight initialization optional to speed VGG construction. This makes loading pre-trained VGG models much faster
Accelerate transforms.adjust_gamma by using PIL's point function instead of custom numpy-based implementation

New Datasets

EMNIST - an extension of MNIST for hand-written letters
OMNIGLOT - a dataset for one-shot learning, with 1623 different handwritten characters from 50 different alphabets
Add a DatasetFolder class - generalization of ImageFolder

Miscellaneous improvements

FakeData accepts a seed argument, so having multiple different FakeData instances is now possible
Use consistent datatypes in Dataset targets. Now all datasets that returns labels will have them as int
Add probability parameter in RandomHorizontalFlip and RandomHorizontalFlip
Replace np.random by random in transforms - improves reproducibility in multi-threaded environments with default arguments
Detect tif images in ImageFolder
Add pad_if_needed to RandomCrop, so that if the crop size is larger than the image, the image is automatically padded
Add support in transforms.ToTensor for PIL Images with mode '1'

Bugfixes

Fix passing list of tensors to utils.save_image
single images passed to make_grid now are now also normalized
Fix PIL img close warnings
Added missing weight initializations to densenet
Avoid division by zero in make_grid when the image is constant
Fix ToTensor when PIL Image has mode F
Fix bug with to_tensor when the input is numpy array of type np.float32.

Assets 2

27 Nov 18:09

alykhantejani

v0.2.0

c9b8914

v0.2.0: New transforms + a new functional interface

This version introduced a functional interface to the transforms, allowing for joint random transformation of inputs and targets. We also introduced a few breaking changes to some datasets and transforms (see below for more details).

Transforms

We have introduced a functional interface for the torchvision transforms, available under torchvision.transforms.functional. This now makes it possible to do joint random transformations on inputs and targets, which is especially useful in tasks like object detection, segmentation and super resolution. For example, you can now do the following:

from torchvision import transforms
import torchvision.transforms.functional as F
import random

def my_segmentation_transform(input, target):
	i, j, h, w = transforms.RandomCrop.get_params(input, (100, 100))
	input = F.crop(input, i, j, h, w)
	target = F.crop(target, i, j, h, w)
	if random.random() > 0.5:
		input = F.hflip(input)
		target = F.hflip(target)
	F.to_tensor(input), F.to_tensor(target)
	return input, target

The following transforms have also been added:

F.vflip and RandomVerticalFlip
FiveCrop and TenCrop
Various color transformations:
- ColorJitter
- F.adjust_brightness
- F.adjust_contrast
- F.adjust_saturation
- F.adjust_hue
LinearTransformation for applications such as whitening
Grayscale and RandomGrayscale
Rotate and RandomRotation
ToPILImage now supports RGBA images
ToPILImage now accepts a mode argument so you can specify which colorspace the image should be
RandomResizedCrop now accepts scale and ratio ranges as input parameters

Documentation

Documentation is now auto generated and publishing to pytorch.org

Datasets:

SEMEION Dataset of handwritten digits added
Phototour dataset patches computed via multi-scale Harris corners now available by setting name equal to notredame_harris, yosemite_harris or liberty_harris in the Phototour dataset

Bug fixes:

Pre-trained densenet models is now CPU compatible #251