requirements.txt and download scripts

.gitignore

@@ -0,0 +1,11 @@
+lib/
+bin/
+include/
+local/
+share/
+pip-selfcheck.json
+data/
+pycocotools/
+pycocoevalcap/
+results/**/checkpoint
+results/**/model*

README.md

@@ -6,6 +6,9 @@ This paper investigates whether the RNN should be viewed as a sequence generator
 
 Works on both python 2 and python 3 (except for the MSCOCO evaluation toolkit which requires python 2) and uses TensorFlow 1.0.
 
-Python dependencies (install all with pip):
-  * tensorflow
-  * future
+## Setup
+
+*setup.sh* downloads all three datasets (flickr8k, flickr30k and MSCOCO) and install all dependencies via pip.
+Alternatively, you can copy/link existing data to data/flickr8k, data/flickr30k or data/coco and run pip install -r requirements.txt manually.
+
+Before running *experiment.py*, you need to copy the *results/* directory to a different directory. The script fails with existing results and does not override them.

download_scripts/download_coco_data_and_tools.sh

@@ -0,0 +1,9 @@
+#! /bin/bash
+git clone https://github.com/tylin/coco-caption.git
+mv coco-caption/pycocotools pycocotools
+mv coco-caption/pycocoevalcap pycocoevalcap
+mkdir -p data/coco/annotations/
+mkdir data/coco/results/
+mv coco-caption/annotations/captions_val2014.json data/coco/annotations/captions.json
+mv coco-caption/results/captions_val2014_fakecap_results.json data/coco/results/generated_captions.json
+rm -fR coco-caption

download_scripts/download_coco_data_and_tools.sh~

@@ -0,0 +1,10 @@
+#! /bin/bash
+# TODO check if data set from http://cs.stanford.edu/people/karpathy/deepimagesent/
+# can be used with not too much adaptions to code
+
+git clone https://github.com/tylin/coco-caption.git
+mv coco-caption/pycocotools ../pycocotools
+mv coco-caption/pycocoevalcap ../pycocoevalcap
+mkdir -p ../data/coco/
+mv coco-caption/annotations/ ../data/coco/
+rm -fR coco-caption

download_scripts/download_flickr_data.sh

@@ -0,0 +1,12 @@
+#! /bin/bash
+
+function download_data {
+    wget http://cs.stanford.edu/people/karpathy/deepimagesent/$1.zip
+    unzip $1.zip
+    rm $1.zip
+}
+
+mkdir data
+cd data
+download_data flickr8k
+download_data flickr30k

download_scripts/download_flickr_data.sh~

@@ -0,0 +1,12 @@
+#! /bin/bash
+
+function download_data {
+    wget http://cs.stanford.edu/people/karpathy/deepimagesent/$1.zip
+    unzip $1.zip
+    rm $1.zip
+}
+
+mkdir ../data
+cd ../data
+download_data flickr8k
+download_data flickr30k

experiment.py

@@ -16,11 +16,12 @@
 minibatch_size  = 50
 
 results_data_dir   = 'results'
-mscoco_dir         = '.../coco-caption-master' #directory to mscoco evaluation code downloaded from https://github.com/tylin/coco-caption
+
 def get_raw_input_data_dir(dataset):
-    return '.../'+dataset #directory to karpathy flickr8k or flickr30k dataset downloaded from http://cs.stanford.edu/people/karpathy/deepimagesent/
+    return 'data/'+dataset
+
+mscoco_dir = get_raw_input_data_dir("coco")
 
-sys.path.append(mscoco_dir)
 from pycocotools.coco import COCO
 from pycocoevalcap.eval import COCOEvalCap
 
@@ -190,7 +191,7 @@ def parse(data):
 
         ################################################################
         print('Training...')
-
+        
         for layer_size in [ 128, 256, 512 ]:
             for method in [ 'merge', 'inject' ]:
                 for run in range(1, num_runs+1):

requirements.txt

@@ -0,0 +1,4 @@
+future==0.16.0
+tensorflow==1.0.0
+matplotlib==2.1.0rc1
+scikit-image==0.13.0

setup.sh

@@ -0,0 +1,4 @@
+#! /bin/bash
+download_scripts/download_flickr_data.sh
+download_scripts/download_coco_data_and_tools.sh
+pip install -r requirements.txt