本文整理汇总了Python中evaluation.encode_data方法的典型用法代码示例。如果您正苦于以下问题：Python evaluation.encode_data方法的具体用法？Python evaluation.encode_data怎么用？Python evaluation.encode_data使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类evaluation的用法示例。
在下文中一共展示了evaluation.encode_data方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: validate_caption_only
# 需要导入模块: import evaluation [as 别名]
# 或者: from evaluation import encode_data [as 别名]
def validate_caption_only(opt, val_loader, model):
# compute the encoding for all the validation images and captions
img_embs, cap_embs = encode_data(
model, val_loader, opt.log_step, logging.info)
# caption retrieval
(r1, r5, r10, medr, meanr) = i2t_text_only(img_embs, cap_embs, measure=opt.measure)
logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
(r1, r5, r10, medr, meanr))
# sum of recalls to be used for early stopping
currscore = r1 + r5 + r10
# record metrics in tensorboard
tb_logger.log_value('r1', r1, step=model.Eiters)
tb_logger.log_value('r5', r5, step=model.Eiters)
tb_logger.log_value('r10', r10, step=model.Eiters)
tb_logger.log_value('medr', medr, step=model.Eiters)
tb_logger.log_value('meanr', meanr, step=model.Eiters)
tb_logger.log_value('rsum', currscore, step=model.Eiters)
return currscore 示例2: validate
# 需要导入模块: import evaluation [as 别名]
# 或者: from evaluation import encode_data [as 别名]
def validate(opt, val_loader, model):
# compute the encoding for all the validation images and captions
img_embs, cap_embs = encode_data(
model, val_loader, opt.log_step, logging.info)
# caption retrieval
(r1, r5, r10, medr, meanr) = i2t(img_embs, cap_embs, measure=opt.measure)
logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
(r1, r5, r10, medr, meanr))
# image retrieval
(r1i, r5i, r10i, medri, meanr) = t2i(
img_embs, cap_embs, measure=opt.measure)
logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
(r1i, r5i, r10i, medri, meanr))
# sum of recalls to be used for early stopping
currscore = r1 + r5 + r10 + r1i + r5i + r10i
# record metrics in tensorboard
tb_logger.log_value('r1', r1, step=model.Eiters)
tb_logger.log_value('r5', r5, step=model.Eiters)
tb_logger.log_value('r10', r10, step=model.Eiters)
tb_logger.log_value('medr', medr, step=model.Eiters)
tb_logger.log_value('meanr', meanr, step=model.Eiters)
tb_logger.log_value('r1i', r1i, step=model.Eiters)
tb_logger.log_value('r5i', r5i, step=model.Eiters)
tb_logger.log_value('r10i', r10i, step=model.Eiters)
tb_logger.log_value('medri', medri, step=model.Eiters)
tb_logger.log_value('meanr', meanr, step=model.Eiters)
tb_logger.log_value('rsum', currscore, step=model.Eiters)
return currscore 示例3: validate
# 需要导入模块: import evaluation [as 别名]
# 或者: from evaluation import encode_data [as 别名]
def validate(opt, val_loader, model, tb_logger):
# compute the encoding for all the validation images and captions
print("start validate")
model.val_start()
img_embs, cap_embs, cap_masks = encode_data(
model, val_loader, opt.log_step, logging.info)
# caption retrieval
(i2t_r1, i2t_r5, i2t_r10, i2t_medr, i2t_meanr), (t2i_r1, t2i_r5, t2i_r10, t2i_medr, t2i_meanr) = i2t(img_embs, cap_embs, cap_masks, measure=opt.measure, model=model)
logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
(i2t_r1, i2t_r5, i2t_r10, i2t_medr, i2t_meanr))
# image retrieval
#(r1i, r5i, r10i, medri, meanr) = t2i(
# img_embs, cap_embs, measure=opt.measure, model=model)
logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
(t2i_r1, t2i_r5, t2i_r10, t2i_medr, t2i_meanr))
# sum of recalls to be used for early stopping
currscore = i2t_r1 + i2t_r5 + i2t_r10 + t2i_r1 + t2i_r5 + t2i_r10
# record metrics in tensorboard
tb_logger.log_value('i2t_r1', i2t_r1, step=model.Eiters)
tb_logger.log_value('i2t_r5', i2t_r5, step=model.Eiters)
tb_logger.log_value('i2t_r10', i2t_r10, step=model.Eiters)
tb_logger.log_value('i2t_medr', i2t_medr, step=model.Eiters)
tb_logger.log_value('i2t_meanr', i2t_meanr, step=model.Eiters)
tb_logger.log_value('t2i_r1', t2i_r1, step=model.Eiters)
tb_logger.log_value('t2i_r5', t2i_r5, step=model.Eiters)
tb_logger.log_value('t2i_r10', t2i_r10, step=model.Eiters)
tb_logger.log_value('t2i_medr', t2i_medr, step=model.Eiters)
tb_logger.log_value('t2i_meanr', t2i_meanr, step=model.Eiters)
tb_logger.log_value('rsum', currscore, step=model.Eiters)
return currscore 示例4: validate
# 需要导入模块: import evaluation [as 别名]
# 或者: from evaluation import encode_data [as 别名]
def validate(opt, val_loader, model):
# compute the encoding for all the validation images and captions
img_embs, cap_embs, cap_lens = encode_data(
model, val_loader, opt.log_step, logging.info)
img_embs = numpy.array([img_embs[i] for i in range(0, len(img_embs), 5)])
start = time.time()
if opt.cross_attn == 't2i':
sims = shard_xattn_t2i(img_embs, cap_embs, cap_lens, opt, shard_size=128)
elif opt.cross_attn == 'i2t':
sims = shard_xattn_i2t(img_embs, cap_embs, cap_lens, opt, shard_size=128)
else:
raise NotImplementedError
end = time.time()
print("calculate similarity time:", end-start)
# caption retrieval
(r1, r5, r10, medr, meanr) = i2t(img_embs, cap_embs, cap_lens, sims)
logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
(r1, r5, r10, medr, meanr))
# image retrieval
(r1i, r5i, r10i, medri, meanr) = t2i(
img_embs, cap_embs, cap_lens, sims)
logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
(r1i, r5i, r10i, medri, meanr))
# sum of recalls to be used for early stopping
currscore = r1 + r5 + r10 + r1i + r5i + r10i
# record metrics in tensorboard
tb_logger.log_value('r1', r1, step=model.Eiters)
tb_logger.log_value('r5', r5, step=model.Eiters)
tb_logger.log_value('r10', r10, step=model.Eiters)
tb_logger.log_value('medr', medr, step=model.Eiters)
tb_logger.log_value('meanr', meanr, step=model.Eiters)
tb_logger.log_value('r1i', r1i, step=model.Eiters)
tb_logger.log_value('r5i', r5i, step=model.Eiters)
tb_logger.log_value('r10i', r10i, step=model.Eiters)
tb_logger.log_value('medri', medri, step=model.Eiters)
tb_logger.log_value('meanr', meanr, step=model.Eiters)
tb_logger.log_value('rsum', currscore, step=model.Eiters)
return currscore 示例5: test_CAMP_model
# 需要导入模块: import evaluation [as 别名]
# 或者: from evaluation import encode_data [as 别名]
def test_CAMP_model(config_path):
print("OK!")
logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
parser = argparse.ArgumentParser()
#config_path = "./experiments/f30k_cross_attention/config_test.yaml"
with open(config_path) as f:
opt = yaml.load(f)
opt = EasyDict(opt['common'])
vocab = pickle.load(open(os.path.join(opt.vocab_path,
'%s_vocab.pkl' % opt.data_name), 'rb'))
opt.vocab_size = len(vocab)
train_logger = LogCollector()
print("----Start init model----")
CAMP = model.CAMP(opt)
CAMP.logger = train_logger
if opt.resume is not None:
ckp = torch.load(opt.resume)
CAMP.load_state_dict(ckp["model"])
CAMP.train_start()
print("----Model init success----")
"""
fake_img = torch.randn(16, 36, opt.img_dim)
fake_text = torch.ones(16, 32).long()
fake_lengths = torch.Tensor([32] * 16)
fake_pos = torch.ones(16, 32).long()
fake_ids = torch.ones(16).long()
CAMP.train_emb(fake_img, fake_text, fake_lengths,
instance_ids=fake_ids)
print("----Test train_emb success----")
"""
train_loader, val_loader = data.get_loaders(
opt.data_name, vocab, opt.crop_size, 128, 4, opt)
test_loader = data.get_test_loader("test", opt.data_name, vocab, opt.crop_size, 128, 4, opt)
CAMP.val_start()
img_embs, cap_embs, cap_masks = encode_data(
CAMP, test_loader, opt.log_step, logging.info)
(r1, r5, r10, medr, meanr), (r1i, r5i, r10i, medri, meanri), score_matrix= i2t(img_embs, cap_embs, cap_masks, measure=opt.measure,
model=CAMP, return_ranks=True)
logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
(r1, r5, r10, medr, meanr))
logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
(r1i, r5i, r10i, medri, meanri))