GitLab

* Missing include for `dlib::loss_multiclass_log_per_pixel_::label_to_ignore`

I was trying to compile the examples and encountered this issue after moving `rgb_label_image_to_index_label_image` to cpp file. Headers should include all symbols they mention.

* Update pascal_voc_2012.h

Should use the official entrypoint for including dnn stuff.
Co-authored-by: Davis E. King <davis685@gmail.com>

Simple typo in the inception training

* Rename function to disable_duplicative_biases

* rename also the functions in the tests... oops

* wip: layer normalization on cpu

* wip: add cuda implementation, nor working yet

* wip: try to fix cuda implementation

* swap grid_strid_range and grid_strid_range_y: does not work yet

* fix CUDA implementation

* implement cuda gradient

* add documentation, move layer_norm, update bn_visitor

* add tests

* use stddev instead of variance in test (they are both 1, anyway)

* add test for means and invstds on CPU and CUDA

* rename visitor to disable_duplicative_bias

* handle more cases in the visitor_disable_input_bias

* Add tests for visitor_disable_input_bias

Now the user doesn't have to supply a visitor capable of visiting all
layers, but instead just the ones they are interested in.  Also added
visit_computational_layers() and visit_computational_layers_range()
since those capture a very common use case more concisely than
visit_layers().  That is, users generally want to mess with the
computational layers specifically as those are the stateful layers.

* add visitor to remove bias from bn_ inputs (#closes 2155)

* remove unused parameter and make documentation more clear

* remove bias from bn_ layers too and use better name

* let the batch norm keep their bias, use even better name

* be more consistent with impl naming

* remove default constructor

* do not use method to prevent some errors

* add disable bias method to pertinent layers

* update dcgan example

- grammar
- print number of network parameters to be able to check bias is not allocated
- at the end, give feedback to the user about what the discriminator thinks about each generated sample

* fix fc_ logic

* add documentation

* add bias_is_disabled methods and update to_xml

* print use_bias=false when bias is disabled

Changed C-style cast to static_cast.

* Remove outdated comment

That comment was there from when I was using a dnn_trainer to train
the discriminator network.

* Fix case

* fix some warnings when running tests

* rever changes in CMakeLists.txt

* update example make use of newly promoted method

* update tests to make use of newly promoted methods

Promote some of the sub-network methods into the add_loss_layer interface so users don't have to write .subnet() so often.

* 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

* simplify definition by reusing struct template parameter

* put resnet into its own namespace

* fix infer names

* rename struct impl to def

* Add dnn_introduction3_ex

* Add new loss layer for binary loss per pixel

* 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 concat_prev layer, and U-net example for semantic segmentation

* Allow to supply mini-batch size as command-line parameter

* Decrease default mini-batch size from 30 to 24

* Resize t1, if needed

* Use DenseNet-style blocks instead of residual learning

* Increase default mini-batch size to 50

* Increase default mini-batch size from 50 to 60

* Resize even during the backward step, if needed

* Use resize_bilinear_gradient for the backward step

* Fix function call ambiguity problem

* Clear destination before adding gradient

* Works OK-ish

* Add more U-tags

* Tweak default mini-batch size

* Define a simpler network when using Microsoft Visual C++ compiler; clean up the DenseNet stuff (leaving it for a later PR)

* Decrease default mini-batch size from 24 to 23

* Define separate dnn filename for MSVC++ and not

* Add documentation for the resize_to_prev layer; move the implementation so that it comes after mult_prev

* Fix previous typo

* Minor formatting changes

* Reverse the ordering of levels

* Increase the learning-rate stopping criterion back to 1e-4 (was 1e-8)

* Use more U-tags even on Windows

* Minor formatting

* Latest MSVC 2017 builds fast, so there's no need to limit the depth any longer

* Tweak default mini-batch size again

* Even though latest MSVC can now build the extra layers, it does not mean we should add them!

* Fix naming

* Problem: integer overflow when calculating sizes (may happen e.g. with very large images)
Solution: change some types from (unsigned) long to size_t

# Conflicts:
#	dlib/dnn/tensor.h

* Fix the fact that std::numeric_limits<unsigned long>::max() isn't always the same number

* Revert serialization changes

* Review fix: use long long instead of size_t

* From long to long long all the way

* Change more types to (hopefully) make the compiler happy

* Change many more types to size_t

* Change even more types to size_t

* Minor type changes

the code, but it helps visual studio use less RAM when building the example,
and might make appveyor not crash.  It's also a
slightly cleaner way to write the code anyway.

* Exposed jitter_image in Python and added an example

* Return Numpy array directly

* Require numpy during setup

* Added install of Numpy before builds

* Changed pip install for user only due to security issues.

* Removed malloc

* Made presence of Numpy during compile optional.

* Conflict

* Refactored get_face_chip/get_face_chips to use Numpy as well.