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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
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