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March 25, 2016 18:13
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Makefile to download, install and run VQA evaluation (and training) code https://github.com/VT-vision-lab/VQA_LSTM_CNN
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| .PHONY: all clean evaluate downloaddata torch | |
| SHELL:=bash -l | |
| ######################################################################## | |
| # Makefile to download, install and run VQA evaluation (and training) code https://github.com/VT-vision-lab/VQA_LSTM_CNN | |
| # Usage: | |
| # make evaluate # Runs evaluation code from downloaded models | |
| # make downloaddata # Downloads MSCOCO dataset to required directory format | |
| # make evaluate DO_TRAINING=1 # Runs training code before evaluation | |
| # make clean # Removes the downloaded models | |
| # make torch # Installs torch | |
| ######################################################################## | |
| # Config | |
| # Do training before evaluation or just download pretrained models? | |
| DO_TRAINING?= | |
| # Data directory for MSCOCO dataset Images, Annotations and Questions | |
| # Scroll down to the definition of target "downloaddata" | |
| # for the required directory structure. | |
| DATA_DIR?=$(if $(call checkos,flux),/scratch/eecs542w16_fluxg/$(USER)/MSCOCO/,/home/$(USER)/data/MSCOCO/) | |
| # Install location for TORCH | |
| TORCH_INSTALL?=$(HOME)/share/torch/ | |
| ######################################################################## | |
| # Environment | |
| PATH:=$(HOME)/.local/bin/:$(PATH) | |
| PYTHONPATH:=$(HOME)/.local/lib/python2.7/site-packages/:$(PYTHONPATH) | |
| all: evaluate | |
| evaluate: VQA/Results/OpenEnded_mscoco_lstm_accuracy.json | |
| clean: | |
| rm data_img.h5 data_prepro.h5 data_prepro.json model/lstm.t7 | |
| torch: $(TORCH_INSTALL)/.installdone | |
| DATA_TRAIN_URL:=https://filebox.ece.vt.edu/%7Ejiasenlu/codeRelease/vqaRelease/train_only/data_train_val.zip | |
| PRETRAINED_LSTM_MODEL_URL:= https://filebox.ece.vt.edu/%7Ejiasenlu/codeRelease/vqaRelease/train_only/pretrained_lstm_train_val.t7.zip | |
| ######################################################################## | |
| # Evaluation | |
| ######################################################################## | |
| # 1. Prepare the features | |
| data_img.h5 data_prepro.h5 data_prepro.json: $(if $(DO_TRAINING),data/vqa_raw_train.json data/vqa_raw_test.json,) $(HOME)/nltk_data/.touch | |
| ifeq ($(strip $(DO_TRAINING)),) | |
| wget $(DATA_TRAIN_URL) -O data_train_val.zip | |
| unzip data_train_val.zip | |
| else | |
| $(modload) && python prepro.py --input_train_json data/vqa_raw_train.json --input_test_json data/vqa_raw_test.json --num_ans 1000 | |
| endif | |
| $(HOME)/nltk_data/.touch: | |
| $(modload) && python -c "import nltk; nltk.download('all')" | |
| touch $@ | |
| # 2. Prepare the LSTM trained model | |
| model/lstm.t7: $(if $(DO_TRAINING),model/VGG_ILSVRC_19_layers.caffemodel model/VGG_ILSVRC_19_layers.prototxt torch,) | |
| ifeq ($(strip $(DO_TRAINING)),) | |
| mkdir -p model | |
| wget $(PRETRAINED_LSTM_MODEL_URL) -O pretrained_lstm_train_val.t7.zip | |
| unzip pretrained_lstm_train_val.t7.zip -d model | |
| else | |
| $(torch-act) && th prepro_img.lua -input_json data_prepro.json -image_root $(DATA_DIR)/Images/mscoco/ -cnn_proto model/VGG_ILSVRC_19_layers.prototxt -cnn_model model/VGG_ILSVRC_19_layers.caffemodel | |
| $(torch-act) && th train.lua | |
| mv model/save/lstm.t7 model/lstm.t7 | |
| endif | |
| # 3. Run evaluations on the downloaded features and pretrained LSTM model | |
| result/MultipleChoice_mscoco_lstm_results.json result/OpenEnded_mscoco_lstm_results.json: data_img.h5 data_prepro.h5 data_prepro.json model/lstm.t7 torch | |
| $(torch-act) && th eval.lua -input_img_h5 data_img.h5 -input_ques_h5 data_prepro.h5 -input_json data_prepro.json -model_path model/lstm.t7 | |
| # 4. Clone the evaluation repository | |
| VQA/README.md: | |
| git clone https://github.com/VT-vision-lab/VQA.git VQA | |
| VQA/Annotations: VQA/README.md | |
| ln -s $(DATA_DIR)/Annotations/ VQA/Annotations | |
| VQA/Questions: VQA/README.md | |
| ln -s $(DATA_DIR)/Questions/ VQA/Questions | |
| VQA/Images: VQA/README.md | |
| ln -s $(DATA_DIR)/Images/ VQA/Images | |
| VQA/Results: VQA/README.md | |
| ln -s result/ VQA/Results | |
| VQA/Results/%_mscoco_lstm_accuracy.json: result/%_mscoco_lstm_results.json \ | |
| VQA/README.md VQA/Annotations VQA/Questions VQA/Images VQA/Results | |
| $(modload) && cd VQA/PythonEvaluationTools/ && python vqaEvalDemo.py | |
| ######################################################################## | |
| # Only for training | |
| ######################################################################## | |
| VGG_ILSVRC_19_LAYERS_CAFFEMODEL_URL:=http://www.robots.ox.ac.uk/%7Evgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel | |
| VGG_ILSVRC_19_LAYERS_PROTOTXT_URL:=https://gist.githubusercontent.com/ksimonyan/3785162f95cd2d5fee77/raw/bb2b4fe0a9bb0669211cf3d0bc949dfdda173e9e/VGG_ILSVRC_19_layers_deploy.prototxt | |
| model/VGG_ILSVRC_19_layers.caffemodel: | |
| wget $(VGG_ILSVRC_19_LAYERS_CAFFEMODEL_URL) -O $@ | |
| model/VGG_ILSVRC_19_layers.prototxt: | |
| wget $(VGG_ILSVRC_19_LAYERS_PROTOTXT_URL) -O $@ | |
| data/vqa_raw_train.json data/vqa_raw_test.json: | |
| $(modload) && cd data && python vqa_preprocessing.py --download True --split 1 | |
| ######################################################################## | |
| # Data download | |
| ######################################################################## | |
| ANNOTATION_TRAIN_URL:=http://visualqa.org/data/mscoco/vqa/Annotations_Train_mscoco.zip | |
| ANNOTATION_VALIDATION_URL:=http://visualqa.org/data/mscoco/vqa/Annotations_Val_mscoco.zip | |
| QUESTIONS_TRAIN_URL:=http://visualqa.org/data/mscoco/vqa/Questions_Train_mscoco.zip | |
| QUESTIONS_VALIDATION_URL:=http://visualqa.org/data/mscoco/vqa/Questions_Val_mscoco.zip | |
| QUESTIONS_TEST_URL:=http://visualqa.org/data/mscoco/vqa/Questions_Test_mscoco.zip | |
| MSCOCO_TRAIN_IMAGES_URL:=http://msvocds.blob.core.windows.net/coco2014/train2014.zip | |
| MSCOCO_VALIDATION_URL:=http://msvocds.blob.core.windows.net/coco2014/val2014.zip | |
| MSCOCO_TEST_URL:=http://msvocds.blob.core.windows.net/coco2015/test2015.zip | |
| # Canned recipe for downloading and unziping | |
| define wget-data-to-dir | |
| mkdir -p $2 | |
| wget $1 -O tmp.zip | |
| unzip tmp.zip -d $2 | |
| endef | |
| # Required data organization | |
| downloaddata: \ | |
| $(DATA_DIR)/Annotations/mscoco_train2014_annotations.json\ | |
| $(DATA_DIR)/Annotations/mscoco_val2014_annotations.json\ | |
| $(DATA_DIR)/Questions/OpenEnded_mscoco_train2014_questions.json\ | |
| $(DATA_DIR)/Questions/OpenEnded_mscoco_val2014_questions.json\ | |
| $(DATA_DIR)/Questions/MultipleChoice_mscoco_train2014_questions.json\ | |
| $(DATA_DIR)/Questions/MultipleChoice_mscoco_val2014_questions.json\ | |
| $(DATA_DIR)/Images/mscoco/train2014/COCO_train2014_000000581921.jpg\ | |
| $(DATA_DIR)/Images/mscoco/val2014/COCO_val2014_000000581929.jpg\ | |
| $(DATA_DIR)/Images/mscoco/test2015/COCO_test2015_000000581923.jpg | |
| $(DATA_DIR)/Annotations/mscoco_train2014_annotations.json: | |
| $(call wget-data-to-dir,$(ANNOTATION_TRAIN_URL),$(dir $@)) | |
| $(DATA_DIR)/Annotations/mscoco_val2014_annotations.json: | |
| $(call wget-data-to-dir,$(ANNOTATION_VALIDATION_URL),$(dir $@)) | |
| $(DATA_DIR)/Questions/OpenEnded_mscoco_train2014_questions.json $(DATA_DIR)/Questions/MultipleChoice_mscoco_train2014_questions.json: | |
| $(call wget-data-to-dir,$(QUESTIONS_TRAIN_URL),$(dir $@)) | |
| $(DATA_DIR)/Questions/OpenEnded_mscoco_val2014_questions.json $(DATA_DIR)/Questions/MultipleChoice_mscoco_val2014_questions.json: | |
| $(call wget-data-to-dir,$(QUESTIONS_VALIDATION_URL),$(dir $@)) | |
| $(DATA_DIR)/Images/mscoco/train2014/COCO_train2014_000000581921.jpg: | |
| $(call wget-data-to-dir,$(MSCOCO_TRAIN_IMAGES_URL),$(abspath $(dir $@)../)) | |
| $(DATA_DIR)/Images/mscoco/val2014/COCO_val2014_000000581929.jpg: | |
| $(call wget-data-to-dir,$(MSCOCO_VALIDATION_URL),$(abspath $(dir $@)../)) | |
| $(DATA_DIR)/Images/mscoco/test2015/COCO_test2015_000000581923.jpg: | |
| $(call wget-data-to-dir,$(MSCOCO_TEST_URL),$(abspath $(dir $@)../)) | |
| ######################################################################## | |
| # Torch install | |
| ######################################################################## | |
| $(TORCH_INSTALL)/README.md: | |
| mkdir -p $(dir $@) | |
| git clone https://github.com/torch/distro.git $(dir $@) --recursive | |
| CC:=$(shell which gcc) | |
| CXX:=$(shell which g++) | |
| export CC CXX | |
| $(TORCH_INSTALL)/install/bin/th $(TORCH_INSTALL)/.installdone: $(TORCH_INSTALL)/README.md | |
| ifeq ($(call checkos,Ubuntu),Ubuntu) | |
| cd $(dir $^) && bash install-deps | |
| else ifeq ($(call checkos,flux),flux) | |
| $(modload) && pip install --user pillow nltk | |
| $(modload) && CPATH=$$(HDF5_INC) LIBRARY_PATH=$$(HDF5_LINK) pip install --user h5py | |
| endif | |
| $(modload) && cd $(dir $^) && ./install.sh | |
| touch $(TORCH_INSTALL)/.installdone | |
| # Check OS | |
| checkos=$(findstring $1,$(shell uname -a)) | |
| # Load env | |
| modload:=$(if $(call checkos,flux),module load cmake gcc/4.7.0 cuda/6.5 git gnuplot python openmpi cudnn hdf5/1.8.9/gcc/4.7.0,true) | |
| # Activate torch related environment variable | |
| define torch-act | |
| source $(TORCH_INSTALL)/install/bin/torch-activate | |
| endef |
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