我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用tensorflow.python.ops.array_ops.reverse_v2()。
def random_flip_left_right(image, bboxes, seed=None): """Random flip left-right of an image and its bounding boxes. """ def flip_bboxes(bboxes): """Flip bounding boxes coordinates. """ bboxes = tf.stack([bboxes[:, 0], 1 - bboxes[:, 3], bboxes[:, 2], 1 - bboxes[:, 1]], axis=-1) return bboxes # Random flip. Tensorflow implementation. with tf.name_scope('random_flip_left_right'): image = ops.convert_to_tensor(image, name='image') _Check3DImage(image, require_static=False) uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed) mirror_cond = math_ops.less(uniform_random, .5) # Flip image. result = control_flow_ops.cond(mirror_cond, lambda: array_ops.reverse_v2(image, [1]), lambda: image) # Flip bboxes. bboxes = control_flow_ops.cond(mirror_cond, lambda: flip_bboxes(bboxes), lambda: bboxes) return fix_image_flip_shape(image, result), bboxes
def random_flip_left_right(image, bboxes, seed=None): """Random flip left-right of an image and its bounding boxes. """ def flip_bboxes(bboxes): """Flip bounding boxes coordinates. """ bboxes = tf.stack([bboxes[:, 0], 1 - bboxes[:, 3], bboxes[:, 2], 1 - bboxes[:, 1]], axis=-1) return bboxes # Random flip. Tensorflow implementation. with tf.name_scope('random_flip_left_right'): image = ops.convert_to_tensor(image, name='image') _Check3DImage(image, require_static=False) uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed) mirror_cond = math_ops.less(uniform_random, .5) #debugging info # mirror_cond = tf.Print(mirror_cond, [mirror_cond], 'flipped image') # Flip image. result = control_flow_ops.cond(mirror_cond, lambda: array_ops.reverse_v2(image, [1]), lambda: image) # Flip bboxes. bboxes = control_flow_ops.cond(mirror_cond, lambda: flip_bboxes(bboxes), lambda: bboxes) return fix_image_flip_shape(image, result), bboxes
def _event_shape(self): return array_ops.reverse_v2(array_ops.shape(self.alpha), [0])[0]
def ndlstm_base_dynamic(inputs, noutput, scope=None, reverse=False): """Run an LSTM, either forward or backward. This is a 1D LSTM implementation using dynamic_rnn and the TensorFlow LSTM op. Args: inputs: input sequence (length, batch_size, ninput) noutput: depth of output scope: optional scope name reverse: run LSTM in reverse Returns: Output sequence (length, batch_size, noutput) """ with variable_scope.variable_scope(scope, "SeqLstm", [inputs]): # TODO(tmb) make batch size, sequence_length dynamic # example: sequence_length = tf.shape(inputs)[0] _, batch_size, _ = _shape(inputs) lstm_cell = core_rnn_cell_impl.BasicLSTMCell(noutput, state_is_tuple=False) state = array_ops.zeros([batch_size, lstm_cell.state_size]) sequence_length = int(inputs.get_shape()[0]) sequence_lengths = math_ops.to_int64( array_ops.fill([batch_size], sequence_length)) if reverse: inputs = array_ops.reverse_v2(inputs, [0]) outputs, _ = rnn.dynamic_rnn( lstm_cell, inputs, sequence_lengths, state, time_major=True) if reverse: outputs = array_ops.reverse_v2(outputs, [0]) return outputs
def _auc_convert_hist_to_auc(hist_true_acc, hist_false_acc, nbins): """Convert histograms to auc. Args: hist_true_acc: `Tensor` holding accumulated histogram of scores for records that were `True`. hist_false_acc: `Tensor` holding accumulated histogram of scores for records that were `False`. nbins: Integer number of bins in the histograms. Returns: Scalar `Tensor` estimating AUC. """ # Note that this follows the "Approximating AUC" section in: # Efficient AUC learning curve calculation, R. R. Bouckaert, # AI'06 Proceedings of the 19th Australian joint conference on Artificial # Intelligence: advances in Artificial Intelligence # Pages 181-191. # Note that the above paper has an error, and we need to re-order our bins to # go from high to low score. # Normalize histogram so we get fraction in each bin. normed_hist_true = math_ops.truediv(hist_true_acc, math_ops.reduce_sum(hist_true_acc)) normed_hist_false = math_ops.truediv(hist_false_acc, math_ops.reduce_sum(hist_false_acc)) # These become delta x, delta y from the paper. delta_y_t = array_ops.reverse_v2(normed_hist_true, [0], name='delta_y_t') delta_x_t = array_ops.reverse_v2(normed_hist_false, [0], name='delta_x_t') # strict_1d_cumsum requires float32 args. delta_y_t = math_ops.cast(delta_y_t, dtypes.float32) delta_x_t = math_ops.cast(delta_x_t, dtypes.float32) # Trapezoidal integration, \int_0^1 0.5 * (y_t + y_{t-1}) dx_t y_t = _strict_1d_cumsum(delta_y_t, nbins) first_trap = delta_x_t[0] * y_t[0] / 2.0 other_traps = delta_x_t[1:] * (y_t[1:] + y_t[:nbins - 1]) / 2.0 return math_ops.add(first_trap, math_ops.reduce_sum(other_traps), name='auc') # TODO(langmore) Remove once a faster cumsum (accumulate_sum) Op is available. # Also see if cast to float32 above can be removed with new cumsum. # See: https://github.com/tensorflow/tensorflow/issues/813
