data=$(shell realpath data)

models=$(shell realpath models)

ensure_out_dirs_exists = mkdir -p $(data) $(models)

gt_data_dir = $(shell cat gt_data_dir)

setting = $(shell cat $(setting_dir)/$(2) | shyaml get-value $(1))

gt_data = $(shell cat $(setting_dir)/gt.yaml | shyaml get-value $(1) | xargs -i realpath $(gt_data_dir)/{})

get = $(shell cat $(2) | shyaml get-value $(1))

test_get:

echo '{"hello": "world"}' > /tmp/test_get

test "$(call get,hello,/tmp/test_get)" = "world"

test_gt_data:

echo $(call gt_data,gt_val)

REAL_TAGS_DATASET=$(data)/real_tags.hdf5

# build the real datasets from raw images. Not needed as the real_tags file can

# be download.

real_dataset: $(setting_dir)/real_images.txt

$(ensure_out_dirs_exists)

bb_build_real_tag_dataset --out $(REAL_TAGS_DATASET) \

--force \

$(setting_dir)/real_images.txt

echo '{"path": "$(REAL_TAGS_DATASET)"}' > real_dataset

generate_3d_tags_distribution.json:

bb_default_3d_tags_distribution generate_3d_tags_distribution.json

generated_3d_tags_dir=$(data)/generated_3d_tags/

generate_3d_tag_samples=$(call setting,nb_samples,tag3d_data_set.yaml)

# create the dataset of tags from the simple 3D model

tag3d_data_set: generate_3d_tags_distribution.json $(setting_dir)/tag3d_data_set.yaml

$(ensure_out_dirs_exists)

bb_generate_3d_tags \

--force \

--nb-samples $(generate_3d_tag_samples) \

--dist generate_3d_tags_distribution.json \

$(generated_3d_tags_dir)/generated_3d_tags.hdf5

echo '{"path": "$(generated_3d_tags_dir)/generated_3d_tags.hdf5"}' > tag3d_data_set

network_3d_tag_dir = $(models)/tag3d_network

n3d_units = $(call setting,units,tag3d_network.yaml)

n3d_depth = $(call setting,depth,tag3d_network.yaml)

n3d_epoch = $(call setting,epoch,tag3d_network.yaml)

n3d_nb_dense = $(call setting,nb_dense,tag3d_network.yaml)

n3d_weights =$(network_3d_tag_dir)/network_tags3d_n$(n3d_units)_d$(n3d_depth)_e$(n3d_epoch).hdf5

# trains the neural network to emulate the simple 3D model

tag3d_network: tag3d_data_set $(setting_dir)/tag3d_network.yaml

$(ensure_out_dirs_exists)

bb_train_tag3d_network \

--force \

--3d-tags $(call get,path,tag3d_data_set) \

--units $(n3d_units) \

--depth $(n3d_depth)\

--epoch $(n3d_epoch) \

--nb-dense $(n3d_nb_dense) \

$(network_3d_tag_dir)

test -e $(n3d_weights)

echo '{"weights_path": "$(n3d_weights)"}' > tag3d_network

generator_units = $(call setting,gen-units,rendergan.yaml)

discriminator_units = $(call setting,dis-units,rendergan.yaml)

rendergan_dir = $(models)/rendergan/

# trains the GAN

rendergan: tag3d_network real_dataset $(setting_dir)/rendergan.yaml

$(ensure_out_dirs_exists)

bb_train_rendergan \

--real $(call get,path,real_dataset) \

--nntag3d $(call get,weights_path,tag3d_network) \

--output-dir $(rendergan_dir) \

--force \

--epoch $(call setting,epoch,rendergan.yaml) \

--dis-units $(discriminator_units) \

--gen-units $(generator_units)

echo '{"path": "${rendergan_dir}", "epoch": "300"}' > rendergan

render_gan_models_dir = $(call get,path,rendergan)/models/$(call setting,epoch,artificial_data_set.yaml)/

# creates an artificial dataset by using the GAN

artificial_data_set: rendergan $(setting_dir)/artificial_data_set.yaml

$(ensure_out_dirs_exists)

$(eval artificial_data_set_date := $(shell date -Iseconds))

bb_sample_from_rendergan \

--generator ${render_gan_models_dir}/sample_generator_given_z.hdf5 \

--discriminator ${render_gan_models_dir}/discriminator.hdf5 \

--nb-samples $(call setting,nb_train_samples,artificial_data_set.yaml) \

--selected-outputs "$(call setting,selected_outputs,artificial_data_set.yaml)" \

${data}/artificial_train_set_$(artificial_data_set_date).hdf5

bb_sample_from_rendergan \

--generator ${render_gan_models_dir}/sample_generator_given_z.hdf5 \

--discriminator ${render_gan_models_dir}/discriminator.hdf5 \

--nb-samples $(call setting,nb_test_samples,artificial_data_set.yaml) \

--selected-outputs "$(call setting,selected_outputs,artificial_data_set.yaml)" \

${data}/artificial_test_set_$(artificial_data_set_date).hdf5

echo '{"train_set": "${data}/artificial_train_set_$(artificial_data_set_date).hdf5",\n' \

'"test_set": "${data}/artificial_test_set_$(artificial_data_set_date).hdf5"}' \

> artificial_data_set

decoder_settings=decoder.yaml

# trains a decoder model

decoder_model: artificial_data_set $(setting_dir)/$(decoder_settings) $(setting_dir)/gt.yaml

$(ensure_out_dirs_exists)

mkdir -p $(models)/decoder

bb_train_decoder \

--gt-train $(call gt_data,gt_train,gt.yaml) \

--gt-val $(call gt_data,gt_val,gt.yaml) \

--gt-test $(call gt_data,gt_test,gt.yaml) \

--train-sets $(call get,train_set,artificial_data_set) \

--test-set $(call get,test_set,artificial_data_set) \

--create-output-dir-in $(models)/decoder \

--make-json decoder_model \

$(setting_dir)/$(decoder_settings) \

decoder_evaluation:

bb_evaluate_decoder $(call get,path,decoder_model)

decoder_evaluation_on_real:

bb_evaluate_decoder $(call get,path,decoder_on_real)

decoder: decoder_model decoder_evaluation

decoder_on_real: $(setting_dir)/decoder_on_real.yaml $(setting_dir)/gt.yaml

$(ensure_out_dirs_exists)

mkdir -p $(models)/decoder_on_real

bb_train_decoder \

--gt-val $(call gt_data,gt_train_val,gt.yaml) \

--gt-test $(call gt_data,gt_train_test,gt.yaml) \

--train-sets $(call setting,gt_train,gt.yaml) \

--test-set $(call setting,gt_train,gt.yaml) \

--create-output-dir-in $(models)/decoder_on_real \

--make-json decoder_on_real \

$(setting_dir)/decoder_on_real.yaml \

decoder_combined: $(setting_dir)/decoder_combined.yaml $(setting_dir)/gt.yaml

$(ensure_out_dirs_exists)

mkdir -p $(models)/decoder_combined

bb_train_decoder \

--gt-train $(call gt_data,gt_train,gt.yaml) \

--gt-val $(call gt_data,gt_val,gt.yaml) \

--gt-test $(call gt_data,gt_test,gt.yaml) \

--train-sets $(call get,train_set,artificial_data_set) \

--test-set $(call get,test_set,artificial_data_set) \

--create-output-dir-in $(models)/decoder_combined \

--make-json decoder_combined \

$(setting_dir)/decoder_combined.yaml \

multiple_decoder_data_name = $(call setting,data_name,multiple_decoders.yaml)

multiple_decoders: artificial_data_set $(setting_dir)/multiple_decoders.yaml $(setting_dir)/gt.yaml

bb_train_multiple_decoders \

--gt-val $(call gt_data,gt_val,gt.yaml) \

--gt-test $(call gt_data,gt_test,gt.yaml) \

--train-set $(call get,train_set,artificial_data_set) \

--test-set $(call get,test_set,artificial_data_set) \

--n-jobs $(call setting,n_jobs,multiple_decoders.yaml) \

--n-trails $(call setting,n_trails,multiple_decoders.yaml) \

--data-name "$(multiple_decoder_data_name)" \

$(models)/multi_decoders/$(multiple_decoder_data_name)/