GitLab

* wip: loss goes down when training without a dnn_trainer

if I use a dnn_trainer, it segfaults (also with bigger batch sizes...)

* remove commented code

* fix gradient computation (hopefully)

* fix loss computation

* fix crash in input_rgb_image_pair::to_tensor

* fix alias tensor offset

* refactor loss and input layers and complete the example

* add more data augmentation

* add documentation

* add documentation

* small fix in the gradient computation and reuse terms

* fix warning in comment

* use tensor_tools instead of matrix to compute the gradients

* complete the example program

* add support for mult-gpu

* Update dlib/dnn/input_abstract.h

* Update dlib/dnn/input_abstract.h

* Update dlib/dnn/loss_abstract.h

* Update examples/dnn_self_supervised_learning_ex.cpp

* Update examples/dnn_self_supervised_learning_ex.cpp

* Update examples/dnn_self_supervised_learning_ex.cpp

* Update examples/dnn_self_supervised_learning_ex.cpp

* [TYPE_SAFE_UNION] upgrade (#2443)

* [TYPE_SAFE_UNION] upgrade

* MSVC doesn't like keyword not

* MSVC doesn't like keyword and

* added tests for emplate(), copy semantics, move semantics, swap, overloaded and apply_to_contents with non void return types

* - didn't need is_void anymore
- added result_of_t
- didn't really need ostream_helper or istream_helper
- split apply_to_contents into apply_to_contents (return void) and visit (return anything so long as visitor is publicly accessible)

* - updated abstract file

* - added get_type_t
- removed deserialize_helper dupplicate
- don't use std::decay_t, that's c++14

* - removed white spaces
- don't need a return-statement when calling apply_to_contents_impl()
- use unchecked_get() whenever possible to minimise explicit use of pointer casting. lets keep that to a minimum

* - added type_safe_union_size
- added type_safe_union_size_v if C++14 is available
- added tests for above

* - test type_safe_union_size_v

* testing nested unions with visitors.

* re-added comment

* added index() in abstract file

* - refactored reset() to clear()
- added comment about clear() in abstract file
- in deserialize(), only reset the object if necessary

* - removed unecessary comment about exceptions
- removed unecessary // -------------
- struct is_valid is not mentioned in abstract. Instead rather requiring T to be a valid type, it is ensured!
- get_type and get_type_t are private. Client code shouldn't need this.
- shuffled some functions around
- type_safe_union_size and type_safe_union_size_v are removed. not needed
- reset() -> clear()
- bug fix in deserialize() index counts from 1, not 0
- improved the abstract file

* refactored index() to get_current_type_id() as per suggestion

* maybe slightly improved docs

* - HURRAY, don't need std::result_of or std::invoke_result for visit() to work. Just privately define your own type trait, in this case called return_type and return_type_t. it works!
- apply_to_contents() now always calls visit()

* example with private visitor using friendship with non-void return types.

* Fix up contracts

It can't be a post condition that T is a valid type, since the choice of T is up to the caller, it's not something these functions decide.  Making it a precondition.

* Update dlib/type_safe_union/type_safe_union_kernel_abstract.h

* Update dlib/type_safe_union/type_safe_union_kernel_abstract.h

* Update dlib/type_safe_union/type_safe_union_kernel_abstract.h

* - added more tests for copy constructors/assignments, move constructors/assignments, and converting constructors/assignments
- helper_copy -> helper_forward
- added validate_type<T> in a couple of places

* - helper_move only takes non-const lvalue references. So we are not using std::move with universal references !
- use enable_if<is_valid<T>> in favor of validate_type<T>()

* - use enable_if<is_valid<T>> in favor of validate_type<T>()

* - added is_valid_check<>. This wraps enable_if<is_valid<T>,bool> and makes use of SFINAE more robust
Co-authored-by: pfeatherstone <peter@me>
Co-authored-by: pf <pf@me>
Co-authored-by: Davis E. King <davis685@gmail.com>

* Just minor cleanup of docs and renamed some stuff, tweaked formatting.

* fix spelling error

* fix most vexing parse error
Co-authored-by: Davis E. King <davis@dlib.net>
Co-authored-by: pfeatherstone <45853521+pfeatherstone@users.noreply.github.com>
Co-authored-by: pfeatherstone <peter@me>
Co-authored-by: pf <pf@me>
Co-authored-by: Davis E. King <davis685@gmail.com>

* wip: dcgan-example

* wip: dcgan-example

* update example to use leaky_relu and remove bias from net

* wip

* it works!

* add more comments

* add visualization code

* add example documentation

* rename example

* fix comment

* better comment format

* fix the noise generator seed

* add message to hit enter for image generation

* fix srand, too

* add std::vector overload to update_parameters

* improve training stability

* better naming of variables

make sure it is clear we update the generator with the discriminator's
gradient using fake samples and true labels

* fix comment: generator -> discriminator

* update leaky_relu docs to match the relu ones

* replace not with !

* add Davis' suggestions to make training more stable

* use tensor instead of resizable_tensor

* do not use dnn_trainer for discriminator

* Add dnn_introduction3_ex

* Add instance segmentation example - first version of training code

* Add MMOD options; get rid of the cache approach, and instead load all MMOD rects upfront

* Improve console output

* Set filter count

* Minor tweaking

* Inference - first version, at least compiles!

* Ignore overlapped boxes

* Ignore even small instances

* Set overlaps_ignore

* Add TODO remarks

* Revert "Set overlaps_ignore"

This reverts commit 65adeff1f89af62b10c691e7aa86c04fc358d03e.

* Set result size

* Set label image size

* Take ignore-color into account

* Fix the cropping rect's aspect ratio; also slightly expand the rect

* Draw the largest findings last

* Improve masking of the current instance

* Add some perturbation to the inputs

* Simplify ground-truth reading; fix random cropping

* Read even class labels

* Tweak default minibatch size

* Learn only one class

* Really train only instances of the selected class

* Remove outdated TODO remark

* Automatically skip images with no detections

* Print to console what was found

* Fix class index problem

* Fix indentation

* Allow to choose multiple classes

* Draw rect in the color of the corresponding class

* Write detector window classes to ostream; also group detection windows by class (when ostreaming)

* Train a separate instance segmentation network for each classlabel

* Use separate synchronization file for each seg net of each class

* Allow more overlap

* Fix sorting criterion

* Fix interpolating the predicted mask

* Improve bilinear interpolation: if output type is an integer, round instead of truncating

* Add helpful comments

* Ignore large aspect ratios; refactor the code; tweak some network parameters

* Simplify the segmentation network structure; make the object detection network more complex in turn

* Problem: CUDA errors not reported properly to console
Solution: stop and join data loader threads even in case of exceptions

* Minor parameters tweaking

* Loss may have increased, even if prob_loss_increasing_thresh > prob_loss_increasing_thresh_max_value

* Add previous_loss_values_dump_amount to previous_loss_values.size() when deciding if loss has been increasing

* Improve behaviour when loss actually increased after disk sync

* Revert some of the earlier change

* Disregard dumped loss values only when deciding if learning rate should be shrunk, but *not* when deciding if loss has been going up since last disk sync

* Revert "Revert some of the earlier change"

This reverts commit 6c852124efe6473a5c962de0091709129d6fcde3.

* Keep enough previous loss values, until the disk sync

* Fix maintaining the dumped (now "effectively disregarded") loss values count

* Detect cats instead of aeroplanes

* Add helpful logging

* Clarify the intention and the code

* Review fixes

* Add operator== for the other pixel types as well; remove the inline

* If available, use constexpr if

* Revert "If available, use constexpr if"

This reverts commit 503d4dd3355ff8ad613116e3ffcc0fa664674f69.

* Simplify code as per review comments

* Keep estimating steps_without_progress, even if steps_since_last_learning_rate_shrink < iter_without_progress_thresh

* Clarify console output

* Revert "Keep estimating steps_without_progress, even if steps_since_last_learning_rate_shrink < iter_without_progress_thresh"

This reverts commit 9191ebc7762d17d81cdfc334a80ca9a667365740.

* To keep the changes to a bare minimum, revert the steps_since_last_learning_rate_shrink change after all (at least for now)

* Even empty out some of the previous test loss values

* Minor review fixes

* Can't use C++14 features here

* Do not use the struct name as a variable name

* Add example of semantic segmentation using the PASCAL VOC2012 dataset

* Add note about Debug Information Format when using MSVC

* Make the upsampling layers residual as well

* Fix declaration order

* Use a wider net

* trainer.set_iterations_without_progress_threshold(5000); // (was 20000)

* Add residual_up

* Process entire directories of images (just easier to use)

* Simplify network structure so that builds finish even on Visual Studio (faster, or at all)

* Remove the training example from CMakeLists, because it's too much for the 32-bit MSVC++ compiler to handle

* Remove the probably-now-unnecessary set_dnn_prefer_smallest_algorithms call

* Review fix: remove the batch normalization layer from right before the loss

* Review fix: point out that only the Visual C++ compiler has problems.
Also expand the instructions how to run MSBuild.exe to circumvent the problems.

* Review fix: use dlib::match_endings

* Review fix: use dlib::join_rows. Also add some comments, and instructions where to download the pre-trained net from.

* Review fix: make formatting comply with dlib style conventions.

* Review fix: output training parameters.

* Review fix: remove #ifndef __INTELLISENSE__

* Review fix: use std::string instead of char*

* Review fix: update interpolation_abstract.h to say that extract_image_chips can now take the interpolation method as a parameter

* Fix whitespace formatting

* Add more comments

* Fix finding image files for inference

* Resize inference test output to the size of the input; add clarifying remarks

* Resize net output even in calculate_accuracy

* After all crop the net output instead of resizing it by interpolation

* For clarity, add an empty line in the console output

Made it more obvious that this file is a cmake tutorial.  Also added a few more comments about how to use cmake.

cmake config for dlib enable C++11.

file a little clearer on this issue.

--HG--
rename : examples/dnn_mnist_advanced_ex.cpp => examples/dnn_introduction2_ex.cpp
rename : examples/dnn_mnist_ex.cpp => examples/dnn_introduction_ex.cpp