* 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 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 65adeff1f8.
* 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 6c852124ef.
* 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 503d4dd335.
* 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 9191ebc776.
* 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
