本文整理匯總了Python中tfcode.cmp_utils.rotate_preds方法的典型用法代碼示例。如果您正苦於以下問題：Python cmp_utils.rotate_preds方法的具體用法？Python cmp_utils.rotate_preds怎麽用？Python cmp_utils.rotate_preds使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tfcode.cmp_utils的用法示例。
在下文中一共展示了cmp_utils.rotate_preds方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: running_combine
# 需要導入模塊: from tfcode import cmp_utils [as 別名]
# 或者: from tfcode.cmp_utils import rotate_preds [as 別名]
def running_combine(fss_logits, confs_probs, incremental_locs,
incremental_thetas, previous_sum_num, previous_sum_denom,
previous_max_denom, map_size, num_steps):
# fss_logits is B x N x H x W x C
# confs_logits is B x N x H x W x C
# incremental_locs is B x N x 2
# incremental_thetas is B x N x 1
# previous_sum_num etc is B x 1 x H x W x C
with tf.name_scope('combine_{:d}'.format(num_steps)):
running_sum_nums_ = []; running_sum_denoms_ = [];
running_max_denoms_ = [];
fss_logits_ = tf.unstack(fss_logits, axis=1, num=num_steps)
confs_probs_ = tf.unstack(confs_probs, axis=1, num=num_steps)
incremental_locs_ = tf.unstack(incremental_locs, axis=1, num=num_steps)
incremental_thetas_ = tf.unstack(incremental_thetas, axis=1, num=num_steps)
running_sum_num = tf.unstack(previous_sum_num, axis=1, num=1)[0]
running_sum_denom = tf.unstack(previous_sum_denom, axis=1, num=1)[0]
running_max_denom = tf.unstack(previous_max_denom, axis=1, num=1)[0]
for i in range(num_steps):
# Rotate the previous running_num and running_denom
running_sum_num, running_sum_denom, running_max_denom = rotate_preds(
incremental_locs_[i], incremental_thetas_[i], map_size,
[running_sum_num, running_sum_denom, running_max_denom],
output_valid_mask=False)[0]
# print i, num_steps, running_sum_num.get_shape().as_list()
running_sum_num = running_sum_num + fss_logits_[i] * confs_probs_[i]
running_sum_denom = running_sum_denom + confs_probs_[i]
running_max_denom = tf.maximum(running_max_denom, confs_probs_[i])
running_sum_nums_.append(running_sum_num)
running_sum_denoms_.append(running_sum_denom)
running_max_denoms_.append(running_max_denom)
running_sum_nums = tf.stack(running_sum_nums_, axis=1)
running_sum_denoms = tf.stack(running_sum_denoms_, axis=1)
running_max_denoms = tf.stack(running_max_denoms_, axis=1)
return running_sum_nums, running_sum_denoms, running_max_denoms