forked from aalto-cbir/neuraltalkTheano
/
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
import theano
from theano import config
import theano.tensor as tensor
from imagernn.data_provider import getDataProvider
from imagernn.solver import Solver
from imagernn.imagernn_utils import decodeGenerator, eval_split, eval_split_theano
from imagernn.utils import zipp, unzip
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_npy = checkpoint['model']
dump_folder = params['dump_folder']
if 'use_theano' not in checkpoint_params:
checkpoint_params['use_theano'] = 1
checkpoint_params['use_theano'] = 1
if 'image_feat_size' not in checkpoint_params:
checkpoint_params['image_feat_size'] = 4096
if dump_folder:
print 'creating dump folder ' + dump_folder
os.system('mkdir -p ' + dump_folder)
# fetch the data provider
dp = getDataProvider(checkpoint_params)
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)
if checkpoint_params['use_theano'] == 1:
# Compile and init the theano predictor
BatchGenerator.prepPredictor(model_npy, checkpoint_params,params['beam_size'])
model = BatchGenerator.model_th
print("\nUsing model run for %0.2f epochs with validation perplx at %0.3f\n" % (checkpoint['epoch'], \
checkpoint['perplexity']))
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']}
img['feat'] = np.random.rand(*img['feat'].shape)
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']
if dump_folder:
# copy source file to some folder. This makes it easier to distribute results
# into a webpage, because all images that were predicted on are in a single folder
source_file = img['local_file_path']
target_file = os.path.join(dump_folder, os.path.basename(img['local_file_path']))
os.system('cp %s %s' % (source_file, target_file))
# 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
#import pdb; pdb.set_trace()
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': float(top_prediction[0])}
# Code to save all the other candidates
candlist = []
for ci in xrange(len(top_predictions)-1):
prediction = top_predictions[ci+1] # these are sorted with highest on top
candidate = ' '.join([ixtoword[int(ix)] for ix in prediction[1] if ix > 0]) # ix 0 is the END token, skip that
candlist.append({'text': candidate, 'logprob': float(prediction[0])})
img_blob['candidatelist'] = candlist
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
# if checkpoint_params['use_theano'] == 0:
# gtppl = eval_split('test', dp, model, checkpoint_params, misc, eval_max_images = max_images)
# else:
# gtppl = eval_split_theano('test', dp, model, checkpoint_params, misc, BatchGenerator.f_eval, eval_max_images = max_images) # perform the evaluation on VAL set
# 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')
parser.add_argument('-d', '--dump_folder', type=str, default="", help='dump the relevant images to a separate folder with this name?')
args = parser.parse_args()
params = vars(args) # convert to ordinary dict
print 'parsed parameters:'
print json.dumps(params, indent = 2)
main(params)