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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
import nltk
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/', 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
with open(cache_path, 'w') as outfile:
json.dump({'overall': out, 'imgToEval': imgToEval}, outfile)
return out,imgToEval
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)
# Make sure in the evaluation mode
model.eval()
loader.reset_iterator(split)
n = 0
loss = 0
loss_sum = 0
loss_evals = 1e-8
predictions = []
word_num = np.zeros([loader.vocab_size,])
word_prob = np.zeros([loader.vocab_size,])
tag_acc = np.zeros([4,])
tag_num = np.zeros([4,])
tag_acc_rate = np.zeros([5,])
module_count = np.zeros([4, ])
while True:
data = loader.get_batch(split)
n = n + loader.batch_size
if data.get('labels', None) is not None and verbose_loss:
tmp = [data['labels'], data['masks'], data['mods']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
labels, masks, mods = tmp
tmp = [data['att_feats'], data['att_masks'], data['attr_feats'], data['attr_masks'], data['rela_feats'],
data['rela_masks']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
att_feats, att_masks, attr_feats, attr_masks, rela_feats, rela_masks = tmp
rs_data = {}
rs_data['att_feats'] = att_feats
rs_data['att_masks'] = att_masks
rs_data['attr_feats'] = attr_feats
rs_data['attr_masks'] = attr_masks
rs_data['rela_feats'] = rela_feats
rs_data['rela_masks'] = rela_masks
rs_data['cont_ver'] = 0
# with torch.no_grad():
# loss = crit(model(rs_data, labels), labels[:,1:], masks[:,1:]).item()
loss_sum = loss_sum + loss
loss_evals = loss_evals + 1
tmp = [data['att_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['attr_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['attr_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['rela_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['rela_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['labels'][np.arange(loader.batch_size) * loader.seq_per_img],
data['masks'][np.arange(loader.batch_size) * loader.seq_per_img]
]
tmp = [torch.from_numpy(_).cuda() for _ in tmp]
att_feats, att_masks, attr_feats, attr_masks, rela_feats, rela_masks,\
labels, masks = tmp
rs_data = {}
rs_data['att_feats'] = att_feats
rs_data['att_masks'] = att_masks
rs_data['attr_feats'] = attr_feats
rs_data['attr_masks'] = attr_masks
rs_data['rela_feats'] = rela_feats
rs_data['rela_masks'] = rela_masks
rs_data['cont_ver'] = 0
# forward the model to also get generated samples for each image
with torch.no_grad():
seq_temp, _, tag_temp = model(rs_data, opt=eval_kwargs, mode='sample')
seq = seq_temp.data
sents = utils.decode_sequence(loader.get_vocab(), seq, use_ssg=0)
gt_captions = utils.decode_sequence(loader.get_vocab(), labels[:,1:], use_ssg=0)
for k, sent in enumerate(sents):
entry = {'image_id': data['infos'][k]['id'], 'caption': sent, \
'image_path': data['infos'][k]['file_path']}
entry['gt_caption'] = gt_captions[k]
if eval_kwargs.get('dump_path', 0) == 1:
entry['file_name'] = data['infos'][k]['file_path']
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)
predictions.append(entry)
# 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, scores_each = language_eval(dataset, predictions, eval_kwargs['id'], split)
model.train()
return loss_sum/loss_evals, predictions, lang_stats, scores_each