forked from aalto-cbir/neuraltalkTheano
/
predict_on_images.py
228 lines (193 loc) · 9.56 KB
/
predict_on_images.py
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import argparse
import json
import time
import datetime
import numpy as np
import code
import os
import cPickle as pickle
import math
import scipy.io
from imagernn.solver import Solver
from imagernn.imagernn_utils import decodeGenerator, eval_split
from imagernn.data_provider import prepare_data, loadArbitraryFeatures
from picsom_bin_data import picsom_bin_data
from imagernn.utils import zipp
from copy import copy
"""
This script is used to predict sentences for arbitrary images
"""
def main(params):
# load the checkpoint
if params['multi_model'] == 0:
checkpoint_path = params['checkpoint_path']
print 'loading checkpoint %s' % (checkpoint_path, )
checkpoint = pickle.load(open(checkpoint_path, 'rb'))
checkpoint_params = checkpoint['params']
model_npy = checkpoint['model']
checkpoint_params['use_theano'] = 1
if 'image_feat_size' not in checkpoint_params:
checkpoint_params['image_feat_size'] = 4096
if 'misc' in checkpoint:
misc = checkpoint['misc']
ixtoword = misc['ixtoword']
else:
misc = {}
ixtoword = checkpoint['ixtoword']
misc['wordtoix'] = checkpoint['wordtoix']
checkpoint_params['softmax_smooth_factor'] = params['softmax_smooth_factor']
checkpoint_params['softmax_propogate'] = params['softmax_propogate']
if checkpoint_params.get('class_out_factoring',0) == 1:
checkpoint_params['ixtoclsinfo'] = np.zeros((checkpoint_params['nClasses'],2),dtype=np.int32)
ixtoclsinfo = misc['ixtoclsinfo']
checkpoint_params['ixtoclsinfo'][ixtoclsinfo[:,0]] = ixtoclsinfo[:,1:3]
if checkpoint_params.get('sched_sampling_mode',None) !=None:
checkpoint_params['sched_sampling_mode'] = None
BatchGenerator = decodeGenerator(checkpoint_params)
# Compile and init the theano predictor
BatchGenerator.prepPredictor(model_npy, checkpoint_params, params['beam_size'])
model = BatchGenerator.model_th
else:
BatchGenerator = []
model_npy = []
modelTh = []
checkpoint_params = []
for i,checkpoint_path in enumerate(params['checkpoint_path']):
checkpoint = pickle.load(open(checkpoint_path, 'rb'))
model_npy.append(checkpoint['model'])
checkpoint_params.append(checkpoint['params'])
checkpoint_params[i]['use_theano'] = 1
BatchGenerator.append(decodeGenerator(checkpoint_params[i]))
zipp(model_npy[i],BatchGenerator[i].model_th)
modelTh.append(BatchGenerator[i].model_th)
modelTh[i]['comb_weight'] = 1.0/params['nmodels']
BatchGenerator[0].prepMultiPredictor(modelTh,checkpoint_params,params['beam_size'],params['nmodels'])
# output blob which we will dump to JSON for visualizing the results
blob = {}
blob['params'] = params
blob['checkpoint_params'] = copy(checkpoint_params)
if checkpoint_params.get('class_out_factoring',0) == 1:
blob['checkpoint_params'].pop('ixtoclsinfo')
blob['imgblobs'] = []
# load the tasks.txt file and setupe feature loading
root_path = params['root_path']
img_names_list = open(params['imgList'], 'r').read().splitlines()
auxidxes = []
if len(img_names_list[0].rsplit(',')) > 2:
img_names = [x.rsplit(',')[0] for x in img_names_list]
idxes = [int(x.rsplit(',')[1]) for x in img_names_list]
auxidxes = [int(x.rsplit(',')[2]) for x in img_names_list]
elif len(img_names_list[0].rsplit(',')) > 1:
img_names = [x.rsplit(',')[0] for x in img_names_list]
idxes = [int(x.rsplit(',')[1]) for x in img_names_list]
else:
img_names = img_names_list
idxes = xrange(len(img_names_list))
#if checkpoint_params.get('en_aux_inp',0) and (params.get('aux_inp_file','None') == 'None'):
# raise ValueError('ERROR: please specify auxillary input feature using --aux_inp_file')
# return
# load the features for all images
if checkpoint_params.get('swap_aux') == 0 or auxidxes == []:
features, aux_inp = loadArbitraryFeatures(params, idxes, auxidxes=auxidxes)
else:
features, aux_inp = loadArbitraryFeatures(params, auxidxes, auxidxes=idxes)
N = len(img_names)
# iterate over all images and predict sentences
print("\nUsing model run for %0.2f epochs with validation perplx at %0.3f\n" % (checkpoint['epoch'], \
checkpoint['perplexity']))
kwparams = { 'beam_size' : params['beam_size'] }
jsonFname = 'result_struct_%s.json' % (params['fname_append'] )
save_file = os.path.join(root_path, jsonFname)
for n in xrange(N):
print 'image %d/%d:' % (n, N)
# encode the image
if params['multi_model'] == 0:
D,NN = features.shape
img = {}
img['feat'] = features[:, n]
if checkpoint_params.get('en_aux_inp',0):
img['aux_inp'] = aux_inp[:, n]
img['local_file_path'] =img_names[n]
# perform the work. heavy lifting happens inside
Ys = BatchGenerator.predict([{'image':img}], model, checkpoint_params, **kwparams)
else:
kwparams['nmodels'] = params['nmodels']
batch = []
for i in xrange(params['nmodels']):
img = {}
img['feat'] = features[i][:, n]
if checkpoint_params[i].get('en_aux_inp',0):
img['aux_inp'] = aux_inp[i][:, n]
img['local_file_path'] =img_names[n]
batch.append({'image':img})
Ys = BatchGenerator[0].predictMulti(batch, checkpoint_params, **kwparams)
# build up the output
img_blob = {}
img_blob['img_path'] = img['local_file_path']
# 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
if checkpoint_params.get('reverse_sentence',0) == 0:
candidate = ' '.join([ixtoword[int(ix)] for ix in top_prediction[1] if ix > 0]) # ix 0 is the END token, skip that
else:
candidate = ' '.join([ixtoword[int(ix)] for ix in reversed(top_prediction[1]) if ix > 0]) # ix 0 is the END token, skip that
print 'PRED: (%f) %s' % (float(top_prediction[0]), 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)
if (n%5000) == 1:
print 'writing predictions to %s...' % (save_file, )
json.dump(blob, open(save_file, 'w'))
# dump result struct to file
print 'writing predictions to %s...' % (save_file, )
json.dump(blob, open(save_file, 'w'))
# dump output html
html = ''
for img in blob['imgblobs']:
html += '<img src="%s" height="400"><br>' % (img['img_path'], )
html += '(%f) %s <br><br>' % (img['candidate']['logprob'], img['candidate']['text'])
html_file = 'result_%s.html' % (params['fname_append'])
html_file = os.path.join(root_path, html_file)
print 'writing html result file to %s...' % (html_file, )
open(html_file, 'w').write(html)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('checkpoint_path', type=str, help='the input checkpoint')
parser.add_argument('-i', '--imgList', type=str, default='testimgs.txt', help='file with the list of images to process. Either just filenames or in <filenmae, index> format')
parser.add_argument('-f', '--feat_file', type=str, default='vgg_feats.mat', help='file with the features. We can rightnow process only .mat format')
parser.add_argument('-d', '--dest', dest='root_path', default='example_images', type=str, help='folder to store the output files')
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('--fname_append', type=str, default='', help='str to append to routput files')
parser.add_argument('--aux_inp_file', dest='aux_inp_file', type=str, default=None, help='Is there any auxillary inputs ? If yes indicate file here')
parser.add_argument('--softmax_smooth_factor', dest='softmax_smooth_factor', type=float, default=1.0, help='Is there any auxillary inputs ? If yes indicate file here')
parser.add_argument('--softmax_propogate', dest='softmax_propogate', type=int, default=0, help='Is there any auxillary inputs ? If yes indicate file here')
args = parser.parse_args()
params = vars(args) # convert to ordinary dict
if params['aux_inp_file'] != None:
params['en_aux_inp'] = 1
else:
params['en_aux_inp'] = 0
print 'parsed parameters:'
print json.dumps(params, indent = 2)
params['multi_model'] = 0
if params['checkpoint_path'].rsplit('.')[-1] == 'txt':
# Dealing with multiple models now, Setup the checkpointa and feature filenames
params['multi_model'] = 1
model_list = open(params['checkpoint_path'],'r').read().splitlines()
params['nmodels'] = len(model_list)
params['checkpoint_path'] = ['']*len(model_list)
params['feat_file'] = ['']*len(model_list)
params['aux_inp_file'] = ['']*len(model_list)
for i,m in enumerate(model_list):
cauxFname = m.split(',')
params['checkpoint_path'][i] = cauxFname[0]
params['feat_file'][i] = cauxFname[1].lstrip(' ').rstrip(' ')
params['aux_inp_file'][i] = cauxFname[2].lstrip(' ').rstrip(' ') if len(cauxFname) > 2 else None
print params['checkpoint_path']
main(params)