def modeltest():
#上面函数为测试flask程序使用。
parser = argparse.ArgumentParser(description='Test')
parser.add_argument(
'--iter',
default='-1',
type=int,
help='iter: iteration of the checkpoint to load. Default: 80000')
parser.add_argument(
'--batch_size',
default='1',
type=int,
help='batch_size: batch size for parallel test. Default: 1')
parser.add_argument(
'--cache',
default=False,
type=boolean_string,
help='cache: if set as TRUE all the test data will be loaded at once'
' before the transforming start. Default: FALSE')
opt = parser.parse_args()
m = initialization(conf, test=opt.cache)[0]
# load model checkpoint of iteration opt.iter
print('Loading the model...')
m.load()
print('Transforming...')
time = datetime.now()
probe = m.transform('probe', opt.batch_size)
gallery = m.transform('gallery', opt.batch_size)
return evaluation(probe, gallery)
def validate(opt):
model.eval()
data_iter_val = iter(dataloader_val)
references = []
hypotheses = []
num_show = 0
predictions = []
# for step in range(len(dataloader_val)): # 3551
for step in range(2):
imgid, img, proposals, gt_seqs, input_seqs, cat_embeds, \
imgid_, img_, gt_bboxs_, gt_seq_, cat_embeds_, num = data_iter_val.next()
proposals = proposals[:, :max(int(max(num[:,0])),1),:]
gt_seqs = gt_seqs[:, :max(int(max(num[:,1])),1),:]
cat_embeds = cat_embeds[:, :max(int(max(num[:,2])),1),:]
gt_bboxs_ = gt_bboxs_[:, :max(int(max(num[:,3])),1),:]
cat_embeds_ = cat_embeds_[:, :max(int(max(num[:,4])),1),:]
gt_seqs_lens = torch.LongTensor([[len(seq.nonzero()) for seq in batch] for batch in gt_seqs]).unsqueeze(2) # (batch, 5, 1)
input_seqs = torch.zeros(opt.batch_size, opt.seq_length+1).long()
input_seqs[:,0] = dataset.word_idx['<sos>']
if opt.iscuda:
img = img.cuda()
proposals = proposals.cuda()
gt_seqs = gt_seqs.cuda()
input_seqs = input_seqs.cuda()
cat_embeds = cat_embeds.cuda()
img_ = img_.cuda()
gt_bboxs_ = gt_bboxs_.cuda()
gt_seq_ = gt_seq_.cuda()
cat_embeds_ = cat_embeds_.cuda()
num = num.cuda()
gt_seqs_lens = gt_seqs_lens.cuda()
#eval_opt = {'sample_max':1, 'beam_size': opt.beam_size, 'inference_mode' : True, 'tag_size' : opt.cbs_tag_size}
input_seqs, show_skeleton = model(img, proposals, gt_seqs, input_seqs, cat_embeds, \
img_, gt_bboxs_, gt_seq_, cat_embeds_, num, gt_seqs_lens, 'sample')
'''
BLEU: references = [[ref1a, ref1b, ref1c], [ref2a, ref2b], ...],
hypotheses = [hyp1, hyp2, ...]
'''
# References
for i in imgid:
img_caps = dataset_val.coco_cap[i]
references.append(list(
map(lambda c: [dataset.idx_word[str(w)] for w in c if w not in [dataset.word_idx['<sos>'], 0]],
img_caps))) # remove <start> and pads
# Hypotheses
input_seqs = input_seqs.tolist()
for i,c in enumerate(input_seqs):
input_seqs[i] = input_seqs[i][1:(c.index(dataset.word_idx['<eos>'])+1)] if dataset.word_idx['<eos>'] in c\
else input_seqs[i][1:]
hypotheses.append([dataset.idx_word[str(w)] for w in input_seqs[i] if w not in [dataset.word_idx['<sos>'], 0]]) # remove pads
assert len(references) == len(hypotheses)
'''
COCOeval: {'imang_id':int,
'caption':str}
python 2.7
'''
for k, sent in enumerate(hypotheses[-10:]):
sent = ' '.join(sent) if sent[-1]!='<eos>' else ' '.join(sent[:-1])
entry = {'image_id': int(imgid[k]), 'caption': sent}
predictions.append(entry)
if num_show < 20: # show first 20 predictions
print('image %s: %s' %(imgid[k], entry['caption']))
num_show += 1
if step % 100 == 0:
print('step: %d / %d' %(step+1, len(dataloader_val)))
print('Total image to be evaluated %d' %(len(predictions)))
# BLEU-4 scores
bleu4 = corpus_bleu(references, hypotheses)
# COCOeval score
json.dump(predictions, open(opt.val_res_dir + 'val_res.json', 'w'))
lang_stats = utils.evaluation(predictions, opt.val_cap_path, opt.val_res_dir+'val_res.json')
return bleu4, lang_stats
parser.add_argument(
'--iter',
default='-1',
type=int,
help='iter: iteration of the checkpoint to load. Default: 80000')
parser.add_argument(
'--batch_size',
default='1',
type=int,
help='batch_size: batch size for parallel test. Default: 1')
parser.add_argument(
'--cache',
default=False,
type=boolean_string,
help='cache: if set as TRUE all the test data will be loaded at once'
' before the transforming start. Default: FALSE')
opt = parser.parse_args()
m = initialization(conf, test=opt.cache)[0]
# load model checkpoint of iteration opt.iter
print('Loading the model...')
m.load()
print('Transforming...')
time = datetime.now()
probe = m.transform('probe', opt.batch_size)
gallery = m.transform('gallery', opt.batch_size)
evaluation(probe, gallery)
# load model checkpoint of iteration opt.iter
print('Loading the model of iteration %d...' % opt.iter)
m.load(opt.iter)
#加载给定iter的模型参数。
print('Transforming...')
time = datetime.now()
test = m.transform('test', opt.batch_size)
#opt.batch_size默认是1。
#返回的test变量依次为：
# feature，数据总量*(62*255)，nparray，
# view_list, seq_type_list, label_list。
print('Evaluating...')
acc = evaluation(test, conf['data'])
#evaluation使用的cuda_dist函数没有被显式导入，却没有报错。
print('Evaluation complete. Cost:', datetime.now() - time)
# Print rank-1 accuracy of the best model
# e.g.
# ===Rank-1 (Include identical-view cases)===
# NM: 95.405, BG: 88.284, CL: 72.041
for i in range(1):
print('===Rank-%d (Include identical-view cases)===' % (i + 1))
print('NM: %.3f,\tBG: %.3f,\tCL: %.3f' % (np.mean(
acc[0, :, :, i]), np.mean(acc[1, :, :, i]), np.mean(acc[2, :, :, i])))
# Print rank-1 accuracy of the best model，excluding identical-view cases
# e.g.