forked from karpathy/neuraltalk
/
eval_sentence_predictions.py
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/
eval_sentence_predictions.py
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import argparse
import json
import time
import datetime
import numpy as np
import code
import socket
import os
import cPickle as pickle
import math
from imagernn.data_provider import getDataProvider
from imagernn.solver import Solver
from imagernn.imagernn_utils import decodeGenerator, eval_split
def main(params):
# load the checkpoint
checkpoint_path = params['checkpoint_path']
max_images = params['max_images']
print 'loading checkpoint %s' % (checkpoint_path, )
checkpoint = pickle.load(open(checkpoint_path, 'rb'))
checkpoint_params = checkpoint['params']
dataset = checkpoint_params['dataset']
model = checkpoint['model']
# fetch the data provider
dp = getDataProvider(dataset)
misc = {}
misc['wordtoix'] = checkpoint['wordtoix']
ixtoword = checkpoint['ixtoword']
blob = {} # output blob which we will dump to JSON for visualizing the results
blob['params'] = params
blob['checkpoint_params'] = checkpoint_params
blob['imgblobs'] = []
# iterate over all images in test set and predict sentences
BatchGenerator = decodeGenerator(checkpoint_params)
n = 0
all_references = []
all_candidates = []
for img in dp.iterImages(split = 'test', max_images = max_images):
n+=1
print 'image %d/%d:' % (n, max_images)
references = [' '.join(x['tokens']) for x in img['sentences']] # as list of lists of tokens
kwparams = { 'beam_size' : params['beam_size'] }
Ys = BatchGenerator.predict([{'image':img}], model, checkpoint_params, **kwparams)
img_blob = {} # we will build this up
img_blob['img_path'] = img['local_file_path']
img_blob['imgid'] = img['imgid']
# encode the human-provided references
img_blob['references'] = []
for gtsent in references:
print 'GT: ' + gtsent
img_blob['references'].append({'text': gtsent})
# now evaluate and encode the top prediction
top_predictions = Ys[0] # take predictions for the first (and only) image we passed in
top_prediction = top_predictions[0] # these are sorted with highest on top
candidate = ' '.join([ixtoword[ix] for ix in top_prediction[1] if ix > 0]) # ix 0 is the END token, skip that
print 'PRED: (%f) %s' % (top_prediction[0], candidate)
# save for later eval
all_references.append(references)
all_candidates.append(candidate)
img_blob['candidate'] = {'text': candidate, 'logprob': top_prediction[0]}
blob['imgblobs'].append(img_blob)
# use perl script to eval BLEU score for fair comparison to other research work
# first write intermediate files
print 'writing intermediate files into eval/'
open('eval/output', 'w').write('\n'.join(all_candidates))
for q in xrange(5):
open('eval/reference'+`q`, 'w').write('\n'.join([x[q] for x in all_references]))
# invoke the perl script to get BLEU scores
print 'invoking eval/multi-bleu.perl script...'
owd = os.getcwd()
os.chdir('eval')
os.system('./multi-bleu.perl reference < output')
os.chdir(owd)
# now also evaluate test split perplexity
gtppl = eval_split('test', dp, model, checkpoint_params, misc, eval_max_images = max_images)
print 'perplexity of ground truth words based on dictionary of %d words: %f' % (len(ixtoword), gtppl)
blob['gtppl'] = gtppl
# dump result struct to file
print 'saving result struct to %s' % (params['result_struct_filename'], )
json.dump(blob, open(params['result_struct_filename'], 'w'))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('checkpoint_path', type=str, help='the input checkpoint')
parser.add_argument('-b', '--beam_size', type=int, default=1, help='beam size in inference. 1 indicates greedy per-word max procedure. Good value is approx 20 or so, and more = better.')
parser.add_argument('--result_struct_filename', type=str, default='result_struct.json', help='filename of the result struct to save')
parser.add_argument('-m', '--max_images', type=int, default=-1, help='max images to use')
args = parser.parse_args()
params = vars(args) # convert to ordinary dict
print 'parsed parameters:'
print json.dumps(params, indent = 2)
main(params)