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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.
# ==============================================================================
"""Base class for voxel generation model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import os
import numpy as np
from six.moves import xrange
import tensorflow as tf
import input_generator
import utils
slim = tf.contrib.slim
class Im2Vox(object):
"""Defines the voxel generation model."""
__metaclass__ = abc.ABCMeta
def __init__(self, params):
self._params = params
@abc.abstractmethod
def get_metrics(self, inputs, outputs):
"""Gets dictionaries from metrics to value `Tensors` & update `Tensors`."""
pass
@abc.abstractmethod
def get_loss(self, inputs, outputs):
pass
@abc.abstractmethod
def get_regularization_loss(self, scopes):
pass
def set_params(self, params):
self._params = params
def get_inputs(self,
dataset_dir,
dataset_name,
split_name,
batch_size,
image_size,
vox_size,
is_training=True):
"""Loads data for a specified dataset and split."""
del image_size, vox_size
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 64
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
images, voxels = tf.train.batch(
[inputs['image'], inputs['voxel']],
batch_size=batch_size,
num_threads=8,
capacity=8 * batch_size,
name='batching_queues/%s/%s' % (dataset_name, split_name))
outputs = dict()
outputs['images'] = images
outputs['voxels'] = voxels
outputs['num_samples'] = inputs['num_samples']
return outputs
def preprocess(self, raw_inputs, step_size):
"""Selects the subset of viewpoints to train on."""
(quantity, num_views) = raw_inputs['images'].get_shape().as_list()[:2]
inputs = dict()
inputs['voxels'] = raw_inputs['voxels']
for k in xrange(step_size):
inputs['images_%d' % (k + 1)] = []
inputs['matrix_%d' % (k + 1)] = []
for n in xrange(quantity):
selected_views = np.random.choice(num_views, step_size, replace=False)
for k in xrange(step_size):
view_selected = selected_views[k]
inputs['images_%d' %
(k + 1)].append(raw_inputs['images'][n, view_selected, :, :, :])
tf_matrix = self.get_transform_matrix(view_selected)
inputs['matrix_%d' % (k + 1)].append(tf_matrix)
for k in xrange(step_size):
inputs['images_%d' % (k + 1)] = tf.stack(inputs['images_%d' % (k + 1)])
inputs['matrix_%d' % (k + 1)] = tf.stack(inputs['matrix_%d' % (k + 1)])
return inputs
def get_init_fn(self, scopes):
"""Initialization assignment operator function used while training."""
if not self._params.init_model:
return None
is_trainable = lambda x: x in tf.trainable_variables()
var_list = []
for scope in scopes:
var_list.extend(
filter(is_trainable, tf.contrib.framework.get_model_variables(scope)))
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
self._params.init_model, var_list)
def init_assign_function(sess):
sess.run(init_assign_op, init_feed_dict)
return init_assign_function
def get_train_op_for_scope(self, loss, optimizer, scopes):
"""Train operation function for the given scope used file training."""
is_trainable = lambda x: x in tf.trainable_variables()
var_list = []
update_ops = []
for scope in scopes:
var_list.extend(
filter(is_trainable, tf.contrib.framework.get_model_variables(scope)))
update_ops.extend(tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope))
return slim.learning.create_train_op(
loss,
optimizer,
update_ops=update_ops,
variables_to_train=var_list,
clip_gradient_norm=self._params.clip_gradient_norm)
def write_disk_grid(self,
global_step,
log_dir,
input_images,
gt_projs,
pred_projs,
pred_voxels=None):
"""Function called by TF to save the prediction periodically."""
summary_freq = self._params.save_every
def write_grid(input_images, gt_projs, pred_projs, pred_voxels,
global_step):
"""Native python function to call for writing images to files."""
grid = _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels)
if global_step % summary_freq == 0:
img_path = os.path.join(log_dir, '%s.jpg' % str(global_step))
utils.save_image(grid, img_path)
with open(
os.path.join(log_dir, 'pred_voxels_%s' % str(global_step)),
'w') as fout:
np.save(fout, pred_voxels)
with open(
os.path.join(log_dir, 'input_images_%s' % str(global_step)),
'w') as fout:
np.save(fout, input_images)
return grid
py_func_args = [
input_images, gt_projs, pred_projs, pred_voxels, global_step
]
save_grid_op = tf.py_func(write_grid, py_func_args, [tf.uint8],
'wrtie_grid')[0]
slim.summaries.add_image_summary(
tf.expand_dims(save_grid_op, axis=0), name='grid_vis')
return save_grid_op
def _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels):
"""Build the visualization grid with py_func."""
quantity, img_height, img_width = input_images.shape[:3]
for row in xrange(int(quantity / 3)):
for col in xrange(3):
index = row * 3 + col
input_img_ = input_images[index, :, :, :]
gt_proj_ = gt_projs[index, :, :, :]
pred_proj_ = pred_projs[index, :, :, :]
pred_voxel_ = utils.display_voxel(pred_voxels[index, :, :, :, 0])
pred_voxel_ = utils.resize_image(pred_voxel_, img_height, img_width)
if col == 0:
tmp_ = np.concatenate([input_img_, gt_proj_, pred_proj_, pred_voxel_],
1)
else:
tmp_ = np.concatenate(
[tmp_, input_img_, gt_proj_, pred_proj_, pred_voxel_], 1)
if row == 0:
out_grid = tmp_
else:
out_grid = np.concatenate([out_grid, tmp_], 0)
out_grid = out_grid.astype(np.uint8)
return out_grid
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