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| # Copyright 2017 The TensorFlow Authors All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # ============================================================================== | |
| """Provides metrics used by PTN.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| from six.moves import xrange | |
| import tensorflow as tf | |
| slim = tf.contrib.slim | |
| def add_image_pred_metrics( | |
| inputs, outputs, num_views, upscale_factor): | |
| """Computes the image prediction metrics. | |
| Args: | |
| inputs: Input dictionary of the deep rotator model (model_rotator.py). | |
| outputs: Output dictionary of the deep rotator model (model_rotator.py). | |
| num_views: An integer scalar representing the total number | |
| of different viewpoints for each object in the dataset. | |
| upscale_factor: A float scalar representing the number of pixels | |
| per image (num_channels x image_height x image_width). | |
| Returns: | |
| names_to_values: A dictionary representing the current value | |
| of the metric. | |
| names_to_updates: A dictionary representing the operation | |
| that accumulates the error from a batch of data. | |
| """ | |
| names_to_values = dict() | |
| names_to_updates = dict() | |
| for k in xrange(num_views): | |
| tmp_value, tmp_update = tf.contrib.metrics.streaming_mean_squared_error( | |
| outputs['images_%d' % (k + 1)], inputs['images_%d' % (k + 1)]) | |
| name = 'image_pred/rnn_%d' % (k + 1) | |
| names_to_values.update({name: tmp_value * upscale_factor}) | |
| names_to_updates.update({name: tmp_update}) | |
| return names_to_values, names_to_updates | |
| def add_mask_pred_metrics( | |
| inputs, outputs, num_views, upscale_factor): | |
| """Computes the mask prediction metrics. | |
| Args: | |
| inputs: Input dictionary of the deep rotator model (model_rotator.py). | |
| outputs: Output dictionary of the deep rotator model (model_rotator.py). | |
| num_views: An integer scalar representing the total number | |
| of different viewpoints for each object in the dataset. | |
| upscale_factor: A float scalar representing the number of pixels | |
| per image (num_channels x image_height x image_width). | |
| Returns: | |
| names_to_values: A dictionary representing the current value | |
| of the metric. | |
| names_to_updates: A dictionary representing the operation | |
| that accumulates the error from a batch of data. | |
| """ | |
| names_to_values = dict() | |
| names_to_updates = dict() | |
| for k in xrange(num_views): | |
| tmp_value, tmp_update = tf.contrib.metrics.streaming_mean_squared_error( | |
| outputs['masks_%d' % (k + 1)], inputs['masks_%d' % (k + 1)]) | |
| name = 'mask_pred/rnn_%d' % (k + 1) | |
| names_to_values.update({name: tmp_value * upscale_factor}) | |
| names_to_updates.update({name: tmp_update}) | |
| return names_to_values, names_to_updates | |
| def add_volume_iou_metrics(inputs, outputs): | |
| """Computes the per-instance volume IOU. | |
| Args: | |
| inputs: Input dictionary of the voxel generation model. | |
| outputs: Output dictionary returned by the voxel generation model. | |
| Returns: | |
| names_to_values: metrics->values (dict). | |
| names_to_updates: metrics->ops (dict). | |
| """ | |
| names_to_values = dict() | |
| names_to_updates = dict() | |
| labels = tf.greater_equal(inputs['voxels'], 0.5) | |
| predictions = tf.greater_equal(outputs['voxels_1'], 0.5) | |
| labels = (2 - tf.to_int32(labels)) - 1 | |
| predictions = (3 - tf.to_int32(predictions) * 2) - 1 | |
| tmp_values, tmp_updates = tf.metrics.mean_iou( | |
| labels=labels, | |
| predictions=predictions, | |
| num_classes=3) | |
| names_to_values['volume_iou'] = tmp_values * 3.0 | |
| names_to_updates['volume_iou'] = tmp_updates | |
| return names_to_values, names_to_updates | |