GitLab

* Unify video metadata in VideoClips

* Bugfix

* Make tests a bit more robust

* Improve readability of affine transformation code

* Make shear transformation area preserving

The previous shear implementation did not preserve area, and we
implement a version that does.

The formula used was verified with the following sympy code:

from sympy import Matrix, cos, sin, tan, simplify
from sympy.abc import x, y, phi

Xs = Matrix(
        [[1, -tan(x)],
         [0, 1]]
        )

Ys = Matrix(
        [[1, 0],
         [-tan(y), 1]]
        )

R = Matrix(
        [[cos(phi), -sin(phi)],
         [sin(phi), cos(phi)]]
        )

RSS = Matrix(
        [[cos(phi - y)/cos(y), -cos(phi - y)*tan(x)/cos(y) - sin(phi)],
         [sin(phi - y)/cos(y), -sin(phi - y)*tan(x)/cos(y) + cos(phi)]])

print(simplify(R * Ys * Xs - RSS))

One thing that is not clear (and could be tested) is whether avoiding
the explicit products and calculations in _get_inverse_affine_matrix
really gives performance benefits - compared to doing the explicit
calculation done in _test_transformation.

* Use np.matmul instead of @

The @ syntax is not supported in Python 2.

* Support Exporting Mask Rcnn to ONNX

* update tetst

* add control flow test

* fix

* update test and fix img_shape

* add quantized models

* Modify mobilenet.py documentation and clean up comments
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* Move fuse_model method to QuantizableInvertedResidual and clean up args documentation
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* Restore relu settings to default in resnet.py
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* Fix missing return in forward
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* Fix missing return in forwards
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* Change pretrained -> pretrained_float_models
Replace InvertedResidual with block

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* Update tests to follow similar structure to test_models.py, allowing for modular testing

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* Replace forward method with simple function assignment

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* Fix error in arguments for resnet18

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* pretrained_float_model argument missing for mobilenet

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* reference script for quantization aware training and post training quantization

* reference script for quantization aware training and post training quantization

* set pretrained_float_model as False and explicitly provide float model

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* Address review comments:
1. Replace forward with _forward
2. Use pretrained models in reference train/eval script
3. Modify test to skip if fbgemm is not supported

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* Fix lint errors.
Use _forward for common code between float and quantized models
Clean up linting for reference train scripts
Test over all quantizable models

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* Update default values for args in quantization/train.py

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* Update models to conform to new API with quantize argument
Remove apex in training script, add post training quant as an option
Add support for separate calibration data set.

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* Fix minor errors in train_quantization.py

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* Remove duplicate file

* Bugfix

* Minor improvements on the models

* Expose print_freq to evaluate

* Minor improvements on train_quantization.py

* Ensure that quantized models are created and run on the specified backends
Fix errors in test only mode

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* Add model urls

* Fix errors in quantized model tests.
Speedup creation of random quantized model by removing histogram observers

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* Move setting qengine prior to convert.

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* Fix lint error

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* Add readme.md

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* Readme.md

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* Fix lint

* Add adjustment operations for RGB Tensor Images.

Right now, we have operations on PIL images, but we want to have a version of the opeartions that act directly on Tensor images.

Here, we add such operations for adjust_brightness, adjust_contrast and adjust_saturation.

In PIL, those functions are implemented by generating an degenerate image from the first, and then interpolating them together.
- https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageEnhance.py
- https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Blend.c

A few caveats:
* Since PIL operates on uint8, and the tensor operations might be on float, we can get slightly different values because of int truncation.
* We assume here the images are RGB; in particular, to handle an alpha channel, we need to check whether it is present, in which case we copy it to the final image.

* Keep dtype and use broadcast in adjust operations

- We make our operations have input.dtype == output.dtype, at the cost of
adding a few type checks and branches.

- By using Tensor broadcast, we can simplify the calls to _blend.

* Use is_floating_point to check dtype.

* Remove unpacking in tuple

It seems Python 2 does not support this type of unpacking, so it broke
Python 2 builds. This should fix it.

* Add from __future__ import division for Python 2

* Initial version of README for classification reference scripts

* More context

* test: Updated asserts in test_utils

Updated all raw asserts to corresponding unittest.TestCase.assert. See #1483

* style: Fixed lint check

* Removed unnecessary class variables.

* The integrity of dataset files is now being checked right after the download finished. Thus making sure that a corrupt file is not being extracted. In case of corruption we throw a RuntimeError.

* Added missing md5 hashes to MNIST, FashionMNIST, KMNIST, EMNIST and QMNIST datasets.

* Removed printing of error message when integrity check failed.
Reformulated error message.

* Reformatted code to be lint conform.

* Fixed formatting in utils.py

* Unify video backend interfaces

* Remove reference cycle

* Make functions private and enable tests on OSX

* Disable test if video_reader backend not available

* Lint

* Fix import after refactoring

* Fix lint

* test: Updated asserts in test_onnx

Updated all raw asserts to corresponding unittest.TestCase.assert. See #1483

* test: Refactored AssertionError

Opted for cleaner raise to avoid error type casting and string conversion

* extend DistributedSampler to support group_size

* Fix lint

* correctness test implemented with old test architecture

* reverted an unneeded change, ran flake8

* moving to relative tolerance of 1 part in 10k for classification correctness checks

* going down to 1 part in 1000 for correctness checks bc architecture differences

* one percent relative tolerance

* Try vs2019 toolchain

* ver num

* pass env var

* fix search logic

* Support both VS2017 and VS2019

* Some small fixes

* remove download process

* address comments

* fix logic error

* bug fixes

* removed unused import

* add docstrings

* flake8

* remove download BC

* fix test

* removed unused code

* flake 8

* add MD5 verification before extraction

* add mock to test

* * unify _verify_archive() method and function
* remove force flag for parse_*_archive functions
* cleanup

* flake8

* test: Updated asserts in test_io

Updated all raw asserts to corresponding unittest.TestCase.assert. See #1483

* test: Fixed test_io

In order to use unittest.TestCase for asserts in temp_video, the object has to be passed itself to temp_video.

* test: Updated asserts to AssertionError

Switched initial raw assert to AssertionError.

* test: Changed AssertionError for ValueError

Updated all raw asserts to corresponding unittest.TestCase.assert. See #1483

* Make crop torchscriptable

Relevant #1375

* Invert x and y axis

* fix lint

* Add crop test

* revert deletion of space in functional

* add import random

* add dimension in doc

* add import

* fix flake8

* change to self.assert*

* convert to uint8

* assertTrue

* lint

* doc-build fixed

* deprecation fixes

* deprecation updates

Updated all raw asserts to corresponding unittest.TestCase.assert. See #1483

Updated all raw asserts to corresponding unittest.TestCase.assert. See #1483

* Try enabling Windows CUDA CI

* Cleanups

* Attempt 1

* Attempt 2

* Re-enable other tests

* onnx esport faster rcnn

* test

* address PR comments

* revert unbind workaround

* disable tests for older versions of pytorch

Updated all raw asserts to corresponding unittest.TestCase.assert. See #1483

This reverts commit 1e857d93.

* test: Updated assert in test_transforms

Updated all raw asserts to corresponding unittest.TestCase.assert. See #1483

* test: Fixed linter on test_transforms

* added correctness tests for classification models
* refactored tests for extensibility & usability
* flake8 fixes

* Try fix Windows CircleCI

* Try conda init first

* Use newer orb

* Switch to push-based nightlies.

See https://github.com/pytorch/pytorch/issues/26817

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* disable merge
Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* fixup
Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* vflip and hflip tensor

* vflip and hflip tensor

* changes made

* lint

* lint

* lint failing

* added PSRoiAlign and PSRoiPool with C++ autograd and torch ops

* fixed linter errors

* fixed linter errors 2

* fixed linter errors 3

* Use PyTorch 1.3 for CI

* Try fixing TravisCI

* Revert rebase to master to old state

* Try pinning lastest anaconda that was working, there was a new package out yesterday that might have breaken up things

* Try removing conda update conda