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eval_utils.py
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eval_utils.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import numpy as np
import json
from json import encoder
import random
import string
import time
import os
import sys
import misc.utils as utils
bad_endings = ['a','an','the','in','for','at','of','with','before','after','on','upon','near','to','is','are','am']
bad_endings += ['the']
def count_bad(sen):
sen = sen.split(' ')
if sen[-1] in bad_endings:
return 1
else:
return 0
def language_eval(dataset, preds, model_id, split):
import sys
sys.path.append("coco-caption")
annFile = 'coco-caption/annotations/captions_val2014.json'
from pycocotools.coco import COCO
from pycocoevalcap.eval import COCOEvalCap
# encoder.FLOAT_REPR = lambda o: format(o, '.3f')
if not os.path.isdir('eval_results'):
os.mkdir('eval_results')
cache_path = os.path.join('eval_results/', '.cache_'+ model_id + '_' + split + '.json')
coco = COCO(annFile)
valids = coco.getImgIds()
# filter results to only those in MSCOCO validation set (will be about a third)
preds_filt = [p for p in preds if p['image_id'] in valids]
print('using %d/%d predictions' % (len(preds_filt), len(preds)))
json.dump(preds_filt, open(cache_path, 'w')) # serialize to temporary json file. Sigh, COCO API...
cocoRes = coco.loadRes(cache_path)
cocoEval = COCOEvalCap(coco, cocoRes)
cocoEval.params['image_id'] = cocoRes.getImgIds()
cocoEval.evaluate()
# create output dictionary
out = {}
for metric, score in cocoEval.eval.items():
out[metric] = score
imgToEval = cocoEval.imgToEval
for p in preds_filt:
image_id, caption = p['image_id'], p['caption']
imgToEval[image_id]['caption'] = caption
out['bad_count_rate'] = sum([count_bad(_['caption']) for _ in preds_filt]) / float(len(preds_filt))
outfile_path = os.path.join('eval_results/', model_id + '_' + split + '.json')
with open(outfile_path, 'w') as outfile:
json.dump({'overall': out, 'imgToEval': imgToEval}, outfile)
return out
def eval_split(model, crit, loader, eval_kwargs={}):
verbose = eval_kwargs.get('verbose', True)
verbose_beam = eval_kwargs.get('verbose_beam', 1)
verbose_loss = eval_kwargs.get('verbose_loss', 1)
num_images = eval_kwargs.get('num_images', eval_kwargs.get('val_images_use', -1))
split = eval_kwargs.get('split', 'val')
lang_eval = eval_kwargs.get('language_eval', 0)
dataset = eval_kwargs.get('dataset', 'coco')
beam_size = eval_kwargs.get('beam_size', 1)
remove_bad_endings = eval_kwargs.get('remove_bad_endings', 0)
os.environ["REMOVE_BAD_ENDINGS"] = str(remove_bad_endings) # Use this nasty way to make other code clean since it's a global configuration
# Make sure in the evaluation mode
model.eval()
loader.reset_iterator(split)
n = 0
loss = 0
loss_sum = 0
loss_evals = 1e-8
predictions = []
while True:
data = loader.get_batch(split)
n = n + loader.batch_size
if data.get('labels', None) is not None and verbose_loss:
# forward the model to get loss
tmp = [data['fc_feats'], data['att_feats'], data['labels'], data['masks'], data['att_masks']]
tmp = [_.cuda() if _ is not None else _ for _ in tmp]
fc_feats, att_feats, labels, masks, att_masks = tmp
with torch.no_grad():
loss = crit(model(fc_feats, att_feats, labels, att_masks), labels[:,1:], masks[:,1:]).item()
loss_sum = loss_sum + loss
loss_evals = loss_evals + 1
# forward the model to also get generated samples for each image
# Only leave one feature for each image, in case duplicate sample
tmp = [data['fc_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_masks'][np.arange(loader.batch_size) * loader.seq_per_img] if data['att_masks'] is not None else None]
tmp = [_.cuda() if _ is not None else _ for _ in tmp]
fc_feats, att_feats, att_masks = tmp
# forward the model to also get generated samples for each image
with torch.no_grad():
seq = model(fc_feats, att_feats, att_masks, opt=eval_kwargs, mode='sample')[0].data
# Print beam search
if beam_size > 1 and verbose_beam:
for i in range(loader.batch_size):
print('\n'.join([utils.decode_sequence(loader.get_vocab(), _['seq'].unsqueeze(0))[0] for _ in model.done_beams[i]]))
print('--' * 10)
sents = utils.decode_sequence(loader.get_vocab(), seq)
for k, sent in enumerate(sents):
entry = {'image_id': data['infos'][k]['id'], 'caption': sent}
if eval_kwargs.get('dump_path', 0) == 1:
entry['file_name'] = data['infos'][k]['file_path']
predictions.append(entry)
if eval_kwargs.get('dump_images', 0) == 1:
# dump the raw image to vis/ folder
cmd = 'cp "' + os.path.join(eval_kwargs['image_root'], data['infos'][k]['file_path']) + '" vis/imgs/img' + str(len(predictions)) + '.jpg' # bit gross
print(cmd)
os.system(cmd)
if verbose:
print('image %s: %s' %(entry['image_id'], entry['caption']))
# if we wrapped around the split or used up val imgs budget then bail
ix0 = data['bounds']['it_pos_now']
ix1 = data['bounds']['it_max']
if num_images != -1:
ix1 = min(ix1, num_images)
for i in range(n - ix1):
predictions.pop()
if verbose:
print('evaluating validation preformance... %d/%d (%f)' %(ix0 - 1, ix1, loss))
if data['bounds']['wrapped']:
break
if num_images >= 0 and n >= num_images:
break
lang_stats = None
if lang_eval == 1:
lang_stats = language_eval(dataset, predictions, eval_kwargs['id'], split)
# Switch back to training mode
model.train()
return loss_sum/loss_evals, predictions, lang_stats