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May 17, 2021 10:24
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# coding=utf-8 | |
# Copyright 2021 The Google Research Authors. | |
# | |
# 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. | |
"""Implements a 3d semantic segmentation model.""" | |
import gin | |
import gin.tf | |
import tensorflow as tf | |
from tf3d import base_model | |
from tf3d import standard_fields | |
from tf3d.layers import sparse_voxel_unet | |
from tf3d.losses import classification_losses | |
from tf3d.utils import voxel_utils | |
@gin.configurable | |
class SemanticSegmentationModel(base_model.BaseModel): | |
"""3D UNet sparse voxel network for semantic segmentation. | |
Please refer to the following paper for more details: | |
M. Najibi, G. Lai, A. Kundu, Z. Lu, V. Rathod, T. Funkhouser, C. Pantofaru, | |
D. Ross, L. S. Davis, A. Fathi, | |
'DOPS: Learning to Detect 3D Objects and Predict Their 3D Shapes', CVPR 2020. | |
""" | |
def __init__(self, | |
num_classes, | |
train_dir='/tmp/model/train', | |
summary_log_freq=100): | |
"""A semantic segmentation model based on 3D UNet sparse voxel network. | |
Args: | |
num_classes: A int indicating the number of semantic classes to predict | |
logits. | |
train_dir: A directory path to write tensorboard summary for losses. | |
summary_log_freq: A int of the frequency (as batches) to log summary. | |
Returns: | |
A dictionary containing a predicted tensor per task. The predicted tensors | |
are of size [batch_size, num_voxels, num_task_channels]. | |
""" | |
super().__init__( | |
loss_names_to_functions={ | |
'semantic_loss': classification_losses.classification_loss | |
}, | |
loss_names_to_weights={'semantic_loss': 1.0}, | |
train_dir=train_dir, | |
summary_log_freq=summary_log_freq) | |
task_names_to_num_output_channels = { | |
standard_fields.DetectionResultFields.object_semantic_voxels: | |
num_classes | |
} | |
self.num_classes = num_classes | |
self.sparse_conv_unet = sparse_voxel_unet.SparseConvUNet( | |
task_names_to_num_output_channels=task_names_to_num_output_channels) | |
def call(self, inputs, training=True): | |
"""Runs the model and returns the semantic logits prediction. | |
Args: | |
inputs: A dictionary of tensors containing inputs for the model. | |
training: Whether the model runs in training mode. | |
Returns: | |
A dictionary of tensors containing semantic logits prediction. | |
""" | |
# when not using custom train step, this field becomes 2 dim. | |
inputs[standard_fields.InputDataFields.num_valid_voxels] = tf.reshape( | |
inputs[standard_fields.InputDataFields.num_valid_voxels], [-1]) | |
voxel_inputs = (inputs[standard_fields.InputDataFields.voxel_features], | |
inputs[standard_fields.InputDataFields.voxel_xyz_indices], | |
inputs[standard_fields.InputDataFields.num_valid_voxels]) | |
outputs = self.sparse_conv_unet(voxel_inputs, training=training) | |
# If at eval time, transfer voxel features to points | |
if ((not training) and | |
(standard_fields.InputDataFields.points_to_voxel_mapping in inputs)): | |
inputs[standard_fields.InputDataFields.num_valid_points] = tf.reshape( | |
inputs[standard_fields.InputDataFields.num_valid_points], [-1]) | |
voxel_to_point_mapping = ( | |
standard_fields.get_output_voxel_to_point_field_mapping()) | |
point_tensor_outputs = {} | |
for task_name in outputs: | |
if task_name in voxel_to_point_mapping and outputs[ | |
task_name] is not None: | |
point_tensor_outputs[voxel_to_point_mapping[task_name]] = ( | |
voxel_utils.sparse_voxel_grid_to_pointcloud( | |
voxel_features=outputs[task_name], | |
segment_ids=inputs[ | |
standard_fields.InputDataFields.points_to_voxel_mapping], | |
num_valid_voxels=inputs[ | |
standard_fields.InputDataFields.num_valid_voxels], | |
num_valid_points=inputs[ | |
standard_fields.InputDataFields.num_valid_points])) | |
# include fields used by tensorboard visualization call back. | |
outputs.update(point_tensor_outputs) | |
if training: | |
self.calculate_losses(inputs=inputs, outputs=outputs) | |
return outputs | |
def save(self, **kwargs): | |
super().save(**kwargs) |
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