├── framework.jpg ├── utils ├── evaluation_utils.pyc ├── kitti_archives_to_download.txt ├── evaluate_kitti.py ├── evaluation_utils.py └── filenames │ ├── cityscapes_val_files.txt │ └── eigen_test_files.txt ├── utils.py ├── module.py ├── average_gradients.py ├── ops.py ├── README.md ├── bilinear_sampler.py ├── monodepth_dataloader.py ├── main.py └── model_stereo_depthGAN.py /framework.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrea-pilzer/unsup-stereo-depthGAN/HEAD/framework.jpg -------------------------------------------------------------------------------- /utils/evaluation_utils.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrea-pilzer/unsup-stereo-depthGAN/HEAD/utils/evaluation_utils.pyc -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | import tensorflow as tf 3 | 4 | def abs_criterion(in_, target): 5 | return tf.reduce_mean(tf.abs(in_ - target)) 6 | 7 | 8 | def mae_criterion(in_, target): 9 | return tf.reduce_mean((in_-target)**2) 10 | 11 | 12 | def sce_criterion(logits, labels): 13 | return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels)) 14 | -------------------------------------------------------------------------------- /module.py: -------------------------------------------------------------------------------- 1 | # Code for 2 | # Unsupervised Adversarial Depth Estimation using Cycled Generative Networks 3 | # Andrea Pilzer, Dan Xu, Mihai Puscas, Elisa Ricci, Nicu Sebe 4 | # 5 | # 3DV 2018 Conference, Verona, Italy 6 | # 7 | # parts of the code from https://github.com/mrharicot/monodepth 8 | # 9 | 10 | from __future__ import division 11 | import tensorflow as tf 12 | from ops import * 13 | from utils import * 14 | 15 | def mae_criterion(in_, target): 16 | return tf.reduce_mean((in_-target)**2) 17 | -------------------------------------------------------------------------------- /average_gradients.py: -------------------------------------------------------------------------------- 1 | # Copyright 2015 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from __future__ import absolute_import, division, print_function 16 | import tensorflow as tf 17 | 18 | def average_gradients(tower_grads): 19 | 20 | average_grads = [] 21 | for grad_and_vars in zip(*tower_grads): 22 | # Note that each grad_and_vars looks like the following: 23 | # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) 24 | grads = [] 25 | for g, _ in grad_and_vars: 26 | # Add 0 dimension to the gradients to represent the tower. 27 | expanded_g = tf.expand_dims(g, 0) 28 | 29 | # Append on a 'tower' dimension which we will average over below. 30 | grads.append(expanded_g) 31 | 32 | # Average over the 'tower' dimension. 33 | grad = tf.concat(axis=0, values=grads) 34 | grad = tf.reduce_mean(grad, 0) 35 | 36 | # Keep in mind that the Variables are redundant because they are shared 37 | # across towers. So .. we will just return the first tower's pointer to 38 | # the Variable. 39 | v = grad_and_vars[0][1] 40 | grad_and_var = (grad, v) 41 | average_grads.append(grad_and_var) 42 | return average_grads 43 | -------------------------------------------------------------------------------- /ops.py: -------------------------------------------------------------------------------- 1 | import math 2 | import numpy as np 3 | import tensorflow as tf 4 | import tensorflow.contrib.slim as slim 5 | from tensorflow.python.framework import ops 6 | 7 | from utils import * 8 | 9 | def batch_norm(x, name="batch_norm"): 10 | return tf.contrib.layers.batch_norm(x, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True, scope=name) 11 | 12 | def instance_norm(input, name="instance_norm"): 13 | with tf.variable_scope(name): 14 | depth = input.get_shape()[3] 15 | scale = tf.get_variable("scale", [depth], initializer=tf.random_normal_initializer(1.0, 0.02, dtype=tf.float32)) 16 | offset = tf.get_variable("offset", [depth], initializer=tf.constant_initializer(0.0)) 17 | mean, variance = tf.nn.moments(input, axes=[1,2], keep_dims=True) 18 | epsilon = 1e-5 19 | inv = tf.rsqrt(variance + epsilon) 20 | normalized = (input-mean)*inv 21 | return scale*normalized + offset 22 | 23 | def conv2d(input_, output_dim, ks=4, s=2, stddev=0.02, padding='SAME', name="conv2d"): 24 | with tf.variable_scope(name): 25 | return slim.conv2d(input_, output_dim, ks, s, padding=padding, activation_fn=None, 26 | weights_initializer=tf.truncated_normal_initializer(stddev=stddev), 27 | biases_initializer=None) 28 | 29 | def deconv2d(input_, output_dim, ks=4, s=2, stddev=0.02, name="deconv2d"): 30 | with tf.variable_scope(name): 31 | return slim.conv2d_transpose(input_, output_dim, ks, s, padding='SAME', activation_fn=None, 32 | weights_initializer=tf.truncated_normal_initializer(stddev=stddev), 33 | biases_initializer=None) 34 | 35 | def lrelu(x, leak=0.2, name="lrelu"): 36 | return tf.maximum(x, leak*x) 37 | 38 | def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False): 39 | 40 | with tf.variable_scope(scope or "Linear"): 41 | matrix = tf.get_variable("Matrix", [input_.get_shape()[-1], output_size], tf.float32, 42 | tf.random_normal_initializer(stddev=stddev)) 43 | bias = tf.get_variable("bias", [output_size], 44 | initializer=tf.constant_initializer(bias_start)) 45 | if with_w: 46 | return tf.matmul(input_, matrix) + bias, matrix, bias 47 | else: 48 | return tf.matmul(input_, matrix) + bias 49 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Unsupervised Adversarial Depth Estimation using Cycled Generative Networks 2 | The code for "Unsupervised Adversarial Depth Estimation using Cycled Generative Networks" in 3DV2018 3 | Paper link: https://arxiv.org/pdf/1807.10915.pdf 4 | By Andrea Pilzer, Dan Xu, Mihai Puscas, Elisa Ricci, Nicu Sebe 5 | 6 |

7 | 8 |

9 | 10 | # Content 11 | 12 | The experiments are performed on a HPC server with Python 3.6 and Tensorflow 1.10. 13 | 14 | ## 1. Training and testing 15 | 16 | Training 17 | ```shell 18 | python main.py --dataset kitti --filenames_file utils/filenames/eigen_train_files_png.txt \ 19 | --data_path /path/to/KITTI/ --do_stereo --train_branch b2a 20 | ``` 21 | 22 | Testing 23 | ```shell 24 | python main.py --mode test --dataset kitti --filenames_file utils/filenames/eigen_test_files_png.txt \ 25 | --data_path /path/to/KITTI/ --do_stereo --checkpoint_path my_model/model-5000 26 | ``` 27 | **Please note that there is NO extension after the checkpoint name** 28 | 29 | Evaluation 30 | ```shell 31 | python utils/evaluate_kitti.py --split kitti --predicted_disp_path my_model/disparities.npy \ 32 | --gt_path ~/data/KITTI/ 33 | ``` 34 | 35 | ## 2. Datasets 36 | 37 | We used the KITTI dataset in our experiments. Please refer to a very well written dataset description section of [Monodepth](https://github.com/mrharicot/monodepth/blob/master/readme.md) for data preparation. 38 | 39 | ## 3. Trained model 40 | 41 | The pretrained model can be downloaded from [Google Drive](https://drive.google.com/drive/folders/1dWffc6XSyvxRO_89_jicT-cqJjHbd2-c?usp=sharing). 42 | 43 | ## 4. Citation 44 | Please condiser citing our paper if you find the code is useful for your projects: 45 | 
46 | @inproceedings{pilzer2018unsupervised,
47 |   title={Unsupervised Adversarial Depth Estimation using Cycled Generative Networks},
48 |   author={Pilzer, Andrea and Xu, Dan and Puscas, Mihai and Ricci, Elisa and Sebe, Nicu},
49 |   booktitle={2018 International Conference on 3D Vision (3DV)},
50 |   pages={587--595},
51 |   year={2018},
52 |   organization={IEEE}
53 | }
54 | 
55 | @article{pilzer2019progressive,
56 |   title={Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks},
57 |   author={Pilzer, Andrea and Lathuili{\`e}re, St{\'e}phane and Xu, Dan and Puscas, Mihai Marian and Ricci, Elisa and Sebe, Nicu},
58 |   journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
59 |   year={2019},
60 |   publisher={IEEE}
61 | }
62 | 
63 |
64 | 65 | 66 | -------------------------------------------------------------------------------- /bilinear_sampler.py: -------------------------------------------------------------------------------- 1 | # Code for 2 | # Unsupervised Adversarial Depth Estimation using Cycled Generative Networks 3 | # Andrea Pilzer, Dan Xu, Mihai Puscas, Elisa Ricci, Nicu Sebe 4 | # 5 | # 3DV 2018 Conference, Verona, Italy 6 | # 7 | # parts of the code from https://github.com/mrharicot/monodepth 8 | # 9 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 10 | # Copyright 2017 Modifications Clement Godard. 11 | # 12 | # Licensed under the Apache License, Version 2.0 (the "License"); 13 | # you may not use this file except in compliance with the License. 14 | # You may obtain a copy of the License at 15 | # 16 | # http://www.apache.org/licenses/LICENSE-2.0 17 | # 18 | # Unless required by applicable law or agreed to in writing, software 19 | # distributed under the License is distributed on an "AS IS" BASIS, 20 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 21 | # See the License for the specific language governing permissions and 22 | # limitations under the License. 23 | # ============================================================================== 24 | 25 | from __future__ import absolute_import, division, print_function 26 | import tensorflow as tf 27 | 28 | def bilinear_sampler_1d_h(input_images, x_offset, wrap_mode='border', name='bilinear_sampler', **kwargs): 29 | def _repeat(x, n_repeats): 30 | with tf.variable_scope('_repeat'): 31 | rep = tf.tile(tf.expand_dims(x, 1), [1, n_repeats]) 32 | return tf.reshape(rep, [-1]) 33 | 34 | def _interpolate(im, x, y): 35 | with tf.variable_scope('_interpolate'): 36 | 37 | # handle both texture border types 38 | _edge_size = 0 39 | if _wrap_mode == 'border': 40 | _edge_size = 1 41 | im = tf.pad(im, [[0, 0], [1, 1], [1, 1], [0, 0]], mode='CONSTANT') 42 | x = x + _edge_size 43 | y = y + _edge_size 44 | elif _wrap_mode == 'edge': 45 | _edge_size = 0 46 | else: 47 | return None 48 | 49 | x = tf.clip_by_value(x, 0.0, _width_f - 1 + 2 * _edge_size) 50 | 51 | x0_f = tf.floor(x) 52 | y0_f = tf.floor(y) 53 | x1_f = x0_f + 1 54 | 55 | x0 = tf.cast(x0_f, tf.int32) 56 | y0 = tf.cast(y0_f, tf.int32) 57 | x1 = tf.cast(tf.minimum(x1_f, _width_f - 1 + 2 * _edge_size), tf.int32) 58 | 59 | dim2 = (_width + 2 * _edge_size) 60 | dim1 = (_width + 2 * _edge_size) * (_height + 2 * _edge_size) 61 | base = _repeat(tf.range(_num_batch) * dim1, _height * _width) 62 | base_y0 = base + y0 * dim2 63 | idx_l = base_y0 + x0 64 | idx_r = base_y0 + x1 65 | 66 | im_flat = tf.reshape(im, tf.stack([-1, _num_channels])) 67 | 68 | pix_l = tf.gather(im_flat, idx_l) 69 | pix_r = tf.gather(im_flat, idx_r) 70 | 71 | weight_l = tf.expand_dims(x1_f - x, 1) 72 | weight_r = tf.expand_dims(x - x0_f, 1) 73 | 74 | return weight_l * pix_l + weight_r * pix_r 75 | 76 | def _transform(input_images, x_offset): 77 | with tf.variable_scope('transform'): 78 | # grid of (x_t, y_t, 1), eq (1) in ref [1] 79 | x_t, y_t = tf.meshgrid(tf.linspace(0.0, _width_f - 1.0, _width), 80 | tf.linspace(0.0 , _height_f - 1.0 , _height)) 81 | 82 | x_t_flat = tf.reshape(x_t, (1, -1)) 83 | y_t_flat = tf.reshape(y_t, (1, -1)) 84 | 85 | x_t_flat = tf.tile(x_t_flat, tf.stack([_num_batch, 1])) 86 | y_t_flat = tf.tile(y_t_flat, tf.stack([_num_batch, 1])) 87 | 88 | x_t_flat = tf.reshape(x_t_flat, [-1]) 89 | y_t_flat = tf.reshape(y_t_flat, [-1]) 90 | 91 | x_t_flat = x_t_flat + tf.reshape(x_offset, [-1]) * _width_f 92 | 93 | input_transformed = _interpolate(input_images, x_t_flat, y_t_flat) 94 | 95 | output = tf.reshape( 96 | input_transformed, tf.stack([_num_batch, _height, _width, _num_channels])) 97 | return output 98 | 99 | with tf.variable_scope(name): 100 | _num_batch = tf.shape(input_images)[0] 101 | _height = tf.shape(input_images)[1] 102 | _width = tf.shape(input_images)[2] 103 | _num_channels = tf.shape(input_images)[3] 104 | 105 | _height_f = tf.cast(_height, tf.float32) 106 | _width_f = tf.cast(_width, tf.float32) 107 | 108 | _wrap_mode = wrap_mode 109 | 110 | output = _transform(input_images, x_offset) 111 | return output 112 | -------------------------------------------------------------------------------- /utils/kitti_archives_to_download.txt: -------------------------------------------------------------------------------- 1 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_calib.zip 2 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0001/2011_09_26_drive_0001_sync.zip 3 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0002/2011_09_26_drive_0002_sync.zip 4 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0005/2011_09_26_drive_0005_sync.zip 5 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0009/2011_09_26_drive_0009_sync.zip 6 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0011/2011_09_26_drive_0011_sync.zip 7 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0013/2011_09_26_drive_0013_sync.zip 8 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0014/2011_09_26_drive_0014_sync.zip 9 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0015/2011_09_26_drive_0015_sync.zip 10 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0017/2011_09_26_drive_0017_sync.zip 11 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0018/2011_09_26_drive_0018_sync.zip 12 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0019/2011_09_26_drive_0019_sync.zip 13 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0020/2011_09_26_drive_0020_sync.zip 14 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0022/2011_09_26_drive_0022_sync.zip 15 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0023/2011_09_26_drive_0023_sync.zip 16 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0027/2011_09_26_drive_0027_sync.zip 17 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0028/2011_09_26_drive_0028_sync.zip 18 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0029/2011_09_26_drive_0029_sync.zip 19 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0032/2011_09_26_drive_0032_sync.zip 20 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0035/2011_09_26_drive_0035_sync.zip 21 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0036/2011_09_26_drive_0036_sync.zip 22 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0039/2011_09_26_drive_0039_sync.zip 23 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0046/2011_09_26_drive_0046_sync.zip 24 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0048/2011_09_26_drive_0048_sync.zip 25 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0051/2011_09_26_drive_0051_sync.zip 26 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0052/2011_09_26_drive_0052_sync.zip 27 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0056/2011_09_26_drive_0056_sync.zip 28 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0057/2011_09_26_drive_0057_sync.zip 29 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0059/2011_09_26_drive_0059_sync.zip 30 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0060/2011_09_26_drive_0060_sync.zip 31 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0061/2011_09_26_drive_0061_sync.zip 32 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0064/2011_09_26_drive_0064_sync.zip 33 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0070/2011_09_26_drive_0070_sync.zip 34 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0079/2011_09_26_drive_0079_sync.zip 35 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0084/2011_09_26_drive_0084_sync.zip 36 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0086/2011_09_26_drive_0086_sync.zip 37 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0087/2011_09_26_drive_0087_sync.zip 38 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0091/2011_09_26_drive_0091_sync.zip 39 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0093/2011_09_26_drive_0093_sync.zip 40 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0095/2011_09_26_drive_0095_sync.zip 41 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0096/2011_09_26_drive_0096_sync.zip 42 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0101/2011_09_26_drive_0101_sync.zip 43 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0104/2011_09_26_drive_0104_sync.zip 44 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0106/2011_09_26_drive_0106_sync.zip 45 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0113/2011_09_26_drive_0113_sync.zip 46 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_26_drive_0117/2011_09_26_drive_0117_sync.zip 47 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_28_calib.zip 48 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_28_drive_0001/2011_09_28_drive_0001_sync.zip 49 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_28_drive_0002/2011_09_28_drive_0002_sync.zip 50 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_29_calib.zip 51 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_29_drive_0004/2011_09_29_drive_0004_sync.zip 52 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_29_drive_0026/2011_09_29_drive_0026_sync.zip 53 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_29_drive_0071/2011_09_29_drive_0071_sync.zip 54 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_30_calib.zip 55 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_30_drive_0016/2011_09_30_drive_0016_sync.zip 56 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_30_drive_0018/2011_09_30_drive_0018_sync.zip 57 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_30_drive_0020/2011_09_30_drive_0020_sync.zip 58 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_30_drive_0027/2011_09_30_drive_0027_sync.zip 59 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_30_drive_0028/2011_09_30_drive_0028_sync.zip 60 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_30_drive_0033/2011_09_30_drive_0033_sync.zip 61 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_09_30_drive_0034/2011_09_30_drive_0034_sync.zip 62 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_10_03_calib.zip 63 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_10_03_drive_0027/2011_10_03_drive_0027_sync.zip 64 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_10_03_drive_0034/2011_10_03_drive_0034_sync.zip 65 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_10_03_drive_0042/2011_10_03_drive_0042_sync.zip 66 | http://kitti.is.tue.mpg.de/kitti/raw_data/2011_10_03_drive_0047/2011_10_03_drive_0047_sync.zip 67 | -------------------------------------------------------------------------------- /monodepth_dataloader.py: -------------------------------------------------------------------------------- 1 | # Code for 2 | # Unsupervised Adversarial Depth Estimation using Cycled Generative Networks 3 | # Andrea Pilzer, Dan Xu, Mihai Puscas, Elisa Ricci, Nicu Sebe 4 | # 5 | # 3DV 2018 Conference, Verona, Italy 6 | # 7 | # parts of the code from https://github.com/mrharicot/monodepth 8 | # 9 | 10 | from __future__ import absolute_import, division, print_function 11 | import tensorflow as tf 12 | 13 | def string_length_tf(t): 14 | return tf.py_func(len, [t], [tf.int64]) 15 | 16 | class MonodepthDataloader(object): 17 | """monodepth dataloader""" 18 | 19 | def __init__(self, data_path, filenames_file, params, dataset, mode): 20 | self.data_path = data_path 21 | self.params = params 22 | self.dataset = dataset 23 | self.mode = mode 24 | 25 | self.left_image_batch = None 26 | self.right_image_batch = None 27 | 28 | input_queue = tf.train.string_input_producer([filenames_file], shuffle=False) 29 | line_reader = tf.TextLineReader() 30 | _, line = line_reader.read(input_queue) 31 | 32 | split_line = tf.string_split([line]).values 33 | 34 | # we load only one image for test, except if we trained a stereo model 35 | if mode == 'test' and not self.params.do_stereo: 36 | left_image_path = tf.string_join([self.data_path, split_line[0]]) 37 | left_image_o = self.read_image(left_image_path) 38 | else: 39 | left_image_path = tf.string_join([self.data_path, split_line[0]]) 40 | right_image_path = tf.string_join([self.data_path, split_line[1]]) 41 | left_image_o = self.read_image(left_image_path) 42 | right_image_o = self.read_image(right_image_path) 43 | 44 | if mode == 'train': 45 | # randomly flip images 46 | do_flip = tf.random_uniform([], 0, 1) 47 | left_image = tf.cond(do_flip > 0.5, lambda: tf.image.flip_left_right(right_image_o), lambda: left_image_o) 48 | right_image = tf.cond(do_flip > 0.5, lambda: tf.image.flip_left_right(left_image_o), lambda: right_image_o) 49 | 50 | # randomly augment images 51 | do_augment = tf.random_uniform([], 0, 1) 52 | left_image, right_image = tf.cond(do_augment > 0.5, lambda: self.augment_image_pair(left_image, right_image), lambda: (left_image, right_image)) 53 | 54 | left_image.set_shape( [None, None, 3]) 55 | right_image.set_shape([None, None, 3]) 56 | 57 | # capacity = min_after_dequeue + (num_threads + a small safety margin) * batch_size 58 | min_after_dequeue = 2048 59 | capacity = min_after_dequeue + 4 * params.batch_size 60 | self.left_image_batch, self.right_image_batch = tf.train.shuffle_batch([left_image, right_image], 61 | params.batch_size, capacity, min_after_dequeue, params.num_threads) 62 | 63 | elif mode == 'test': 64 | # randomly flip images 65 | do_flip = tf.random_uniform([], 0, 1) 66 | left_image = tf.cond(do_flip > 2, lambda: tf.image.flip_left_right(right_image_o), lambda: left_image_o) 67 | right_image = tf.cond(do_flip > 2, lambda: tf.image.flip_left_right(left_image_o), lambda: right_image_o) 68 | 69 | # randomly augment images 70 | do_augment = tf.random_uniform([], 0, 1) 71 | left_image, right_image = tf.cond(do_augment > 2, lambda: self.augment_image_pair(left_image, right_image), lambda: (left_image, right_image)) 72 | 73 | left_image.set_shape( [None, None, 3]) 74 | right_image.set_shape([None, None, 3]) 75 | 76 | # capacity = min_after_dequeue + (num_threads + a small safety margin) * batch_size 77 | min_after_dequeue = 2048 78 | capacity = min_after_dequeue + 4 * params.batch_size 79 | self.left_image_batch, self.right_image_batch = tf.train.batch([left_image, right_image], params.batch_size) 80 | # self.left_image_batch = tf.stack([left_image_o, tf.image.flip_left_right(left_image_o)], 0) 81 | # self.left_image_batch.set_shape( [2, None, None, 3]) 82 | 83 | # if self.params.do_stereo: 84 | # self.right_image_batch = tf.stack([right_image_o, tf.image.flip_left_right(right_image_o)], 0) 85 | # self.right_image_batch.set_shape( [2, None, None, 3]) 86 | 87 | def augment_image_pair(self, left_image, right_image): 88 | # randomly shift gamma 89 | random_gamma = tf.random_uniform([], 0.8, 1.2) 90 | left_image_aug = left_image ** random_gamma 91 | right_image_aug = right_image ** random_gamma 92 | 93 | # randomly shift brightness 94 | random_brightness = tf.random_uniform([], 0.5, 2.0) 95 | left_image_aug = left_image_aug * random_brightness 96 | right_image_aug = right_image_aug * random_brightness 97 | 98 | # randomly shift color 99 | random_colors = tf.random_uniform([3], 0.8, 1.2) 100 | white = tf.ones([tf.shape(left_image)[0], tf.shape(left_image)[1]]) 101 | color_image = tf.stack([white * random_colors[i] for i in range(3)], axis=2) 102 | left_image_aug *= color_image 103 | right_image_aug *= color_image 104 | 105 | # saturate 106 | left_image_aug = tf.clip_by_value(left_image_aug, 0, 1) 107 | right_image_aug = tf.clip_by_value(right_image_aug, 0, 1) 108 | 109 | return left_image_aug, right_image_aug 110 | 111 | def read_image(self, image_path): 112 | # tf.decode_image does not return the image size, this is an ugly workaround to handle both jpeg and png 113 | path_length = string_length_tf(image_path)[0] 114 | file_extension = tf.substr(image_path, path_length - 3, 3) 115 | file_cond = tf.equal(file_extension, 'jpg') 116 | 117 | image = tf.cond(file_cond, lambda: tf.image.decode_jpeg(tf.read_file(image_path)), lambda: tf.image.decode_png(tf.read_file(image_path))) 118 | 119 | # if the dataset is cityscapes, we crop the last fifth to remove the car hood 120 | if self.dataset == 'cityscapes': 121 | o_height = tf.shape(image)[0] 122 | crop_height = (o_height * 4) // 5 123 | image = image[:crop_height,:,:] 124 | 125 | image = tf.image.convert_image_dtype(image, tf.float32) 126 | image = tf.image.resize_images(image, [self.params.height, self.params.width], tf.image.ResizeMethod.AREA) 127 | 128 | return image 129 | -------------------------------------------------------------------------------- /utils/evaluate_kitti.py: -------------------------------------------------------------------------------- 1 | # Code for 2 | # Unsupervised Adversarial Depth Estimation using Cycled Generative Networks 3 | # Andrea Pilzer, Dan Xu, Mihai Puscas, Elisa Ricci, Nicu Sebe 4 | # 5 | # 3DV 2018 Conference, Verona, Italy 6 | # 7 | # parts of the code from https://github.com/mrharicot/monodepth 8 | # 9 | 10 | import numpy as np 11 | import cv2 12 | import argparse 13 | from evaluation_utils import * 14 | 15 | parser = argparse.ArgumentParser(description='Evaluation on the KITTI dataset') 16 | parser.add_argument('--split', type=str, help='data split, kitti or eigen', required=True) 17 | parser.add_argument('--predicted_disp_path', type=str, help='path to estimated disparities', required=True) 18 | parser.add_argument('--gt_path', type=str, help='path to ground truth disparities', required=True) 19 | parser.add_argument('--min_depth', type=float, help='minimum depth for evaluation', default=0.001) 20 | parser.add_argument('--max_depth', type=float, help='maximum depth for evaluation', default=80) 21 | parser.add_argument('--eigen_crop', help='if set, crops according to Eigen NIPS14', action='store_true') 22 | parser.add_argument('--garg_crop', help='if set, crops according to Garg ECCV16', action='store_true') 23 | 24 | args = parser.parse_args() 25 | 26 | if __name__ == '__main__': 27 | 28 | pred_disparities = np.load(args.predicted_disp_path) 29 | if args.split == 'kitti': 30 | num_samples = 200 31 | 32 | gt_disparities = load_gt_disp_kitti(args.gt_path) 33 | gt_depths, pred_depths, pred_disparities_resized = convert_disps_to_depths_kitti(gt_disparities, pred_disparities) 34 | 35 | elif args.split == 'cityscapes': 36 | num_samples = 1525 37 | test_files = read_text_lines('/data1/pilzer/monodepth-GAN/utils/filenames/cityscapes_test_files.txt') 38 | #read disparity files 39 | gt_files = [] 40 | im_files = [] 41 | im_sizes = [] 42 | cams = [] 43 | num_probs = 0 44 | for filename in test_files: 45 | filename = filename.split()[0] 46 | split = filename.rsplit('_',1)[0] + '_disparity.png' 47 | im = filename 48 | 49 | if os.path.isfile(args.gt_path + split): 50 | gt_files.append(args.gt_path + split) 51 | im_sizes.append(cv2.imread(args.gt_path + split).shape[:2]) 52 | im_files.append(args.gt_path + im) 53 | cams.append(2) 54 | else: 55 | num_probs += 1 56 | print('{} missing'.format(args.gt_path + im)) 57 | print(str(num_probs) + ' files missing') 58 | 59 | num_test = len(im_files) 60 | gt_depths = [] 61 | pred_depths = [] 62 | for t_id in range(num_samples): 63 | camera_id = cams[t_id] # 2 is left, 3 is right 64 | depth = np.array(cv2.imread(gt_files[t_id],-1)).astype(np.float32) 65 | #crop front of the car in bottom 1/5 66 | crop_height = (im_sizes[t_id][0] * 4) // 5 67 | depth = depth[:crop_height,:] 68 | depth = cv2.resize(depth, (im_sizes[t_id][1], im_sizes[t_id][0]), interpolation=cv2.INTER_LINEAR) 69 | depth = (depth - 1) / 256. 70 | gt_depths.append(depth.astype(np.float32)) 71 | #resize predicted depth 72 | disp_pred = cv2.resize(pred_disparities[t_id], (im_sizes[t_id][1], im_sizes[t_id][0]), interpolation=cv2.INTER_LINEAR) 73 | disp_pred = disp_pred * disp_pred.shape[1] 74 | pred_depths.append(disp_pred) 75 | 76 | elif args.split == 'eigen': 77 | num_samples = 697 78 | test_files = read_text_lines('/data1/pilzer/monodepth-GAN/utils/filenames/eigen_test_files_png.txt') 79 | gt_files, gt_calib, im_sizes, im_files, cams = read_file_data(test_files, args.gt_path) 80 | 81 | num_test = len(im_files) 82 | gt_depths = [] 83 | pred_depths = [] 84 | for t_id in range(num_samples): 85 | camera_id = cams[t_id] # 2 is left, 3 is right 86 | depth = generate_depth_map(gt_calib[t_id], gt_files[t_id], im_sizes[t_id], camera_id, False, True) 87 | gt_depths.append(depth.astype(np.float32)) 88 | 89 | disp_pred = cv2.resize(pred_disparities[t_id], (im_sizes[t_id][1], im_sizes[t_id][0]), interpolation=cv2.INTER_LINEAR) 90 | disp_pred = disp_pred * disp_pred.shape[1] 91 | 92 | # need to convert from disparity to depth 93 | focal_length, baseline = get_focal_length_baseline(gt_calib[t_id], camera_id) 94 | depth_pred = (baseline * focal_length) / disp_pred 95 | depth_pred[np.isinf(depth_pred)] = 0 96 | 97 | pred_depths.append(depth_pred) 98 | 99 | rms = np.zeros(num_samples, np.float32) 100 | log_rms = np.zeros(num_samples, np.float32) 101 | abs_rel = np.zeros(num_samples, np.float32) 102 | sq_rel = np.zeros(num_samples, np.float32) 103 | d1_all = np.zeros(num_samples, np.float32) 104 | a1 = np.zeros(num_samples, np.float32) 105 | a2 = np.zeros(num_samples, np.float32) 106 | a3 = np.zeros(num_samples, np.float32) 107 | 108 | for i in range(num_samples): 109 | 110 | gt_depth = gt_depths[i] 111 | pred_depth = pred_depths[i] 112 | 113 | pred_depth[pred_depth < args.min_depth] = args.min_depth 114 | pred_depth[pred_depth > args.max_depth] = args.max_depth 115 | 116 | if args.split == 'eigen': 117 | mask = np.logical_and(gt_depth > args.min_depth, gt_depth < args.max_depth) 118 | 119 | 120 | if args.garg_crop or args.eigen_crop: 121 | gt_height, gt_width = gt_depth.shape 122 | 123 | # crop used by Garg ECCV16 124 | # if used on gt_size 370x1224 produces a crop of [-218, -3, 44, 1180] 125 | if args.garg_crop: 126 | crop = np.array([0.40810811 * gt_height, 0.99189189 * gt_height, 127 | 0.03594771 * gt_width, 0.96405229 * gt_width]).astype(np.int32) 128 | # crop we found by trial and error to reproduce Eigen NIPS14 results 129 | elif args.eigen_crop: 130 | crop = np.array([0.3324324 * gt_height, 0.91351351 * gt_height, 131 | 0.0359477 * gt_width, 0.96405229 * gt_width]).astype(np.int32) 132 | 133 | crop_mask = np.zeros(mask.shape) 134 | crop_mask[crop[0]:crop[1],crop[2]:crop[3]] = 1 135 | mask = np.logical_and(mask, crop_mask) 136 | 137 | if args.split == 'cityscapes': 138 | mask = np.logical_and(gt_depth > (1/args.max_depth), gt_depth < 125.) 139 | 140 | if args.split == 'kitti': 141 | gt_disp = gt_disparities[i] 142 | mask = gt_disp > 0 143 | pred_disp = pred_disparities_resized[i] 144 | 145 | disp_diff = np.abs(gt_disp[mask] - pred_disp[mask]) 146 | bad_pixels = np.logical_and(disp_diff >= 3, (disp_diff / gt_disp[mask]) >= 0.05) 147 | d1_all[i] = 100.0 * bad_pixels.sum() / mask.sum() 148 | 149 | abs_rel[i], sq_rel[i], rms[i], log_rms[i], a1[i], a2[i], a3[i] = compute_errors(gt_depth[mask], pred_depth[mask]) 150 | 151 | print("{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}".format('abs_rel', 'sq_rel', 'rms', 'log_rms', 'd1_all', 'a1', 'a2', 'a3')) 152 | print("{:10.4f}, {:10.4f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f}".format(abs_rel.mean(), sq_rel.mean(), rms.mean(), log_rms.mean(), d1_all.mean(), a1.mean(), a2.mean(), a3.mean())) 153 | -------------------------------------------------------------------------------- /utils/evaluation_utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os 3 | import cv2 4 | from collections import Counter 5 | import pickle 6 | 7 | def compute_errors(gt, pred): 8 | thresh = np.maximum((gt / pred), (pred / gt)) 9 | a1 = (thresh < 1.25 ).mean() 10 | a2 = (thresh < 1.25 ** 2).mean() 11 | a3 = (thresh < 1.25 ** 3).mean() 12 | 13 | rmse = (gt - pred) ** 2 14 | rmse = np.sqrt(rmse.mean()) 15 | 16 | rmse_log = (np.log(gt) - np.log(pred)) ** 2 17 | rmse_log = np.sqrt(rmse_log.mean()) 18 | 19 | abs_rel = np.mean(np.abs(gt - pred) / gt) 20 | 21 | sq_rel = np.mean(((gt - pred)**2) / gt) 22 | 23 | return abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3 24 | 25 | ############################################################################### 26 | ####################### KITTI 27 | 28 | width_to_focal = dict() 29 | width_to_focal[1242] = 721.5377 30 | width_to_focal[1241] = 718.856 31 | width_to_focal[1224] = 707.0493 32 | width_to_focal[1238] = 718.3351 33 | 34 | def load_gt_disp_kitti(path): 35 | gt_disparities = [] 36 | for i in range(200): 37 | disp = cv2.imread(path + "/training/disp_noc_0/" + str(i).zfill(6) + "_10.png", -1) 38 | disp = disp.astype(np.float32) / 256 39 | gt_disparities.append(disp) 40 | return gt_disparities 41 | 42 | def convert_disps_to_depths_kitti(gt_disparities, pred_disparities): 43 | gt_depths = [] 44 | pred_depths = [] 45 | pred_disparities_resized = [] 46 | 47 | for i in range(len(gt_disparities)): 48 | gt_disp = gt_disparities[i] 49 | height, width = gt_disp.shape 50 | 51 | pred_disp = pred_disparities[i] 52 | pred_disp = width * cv2.resize(pred_disp, (width, height), interpolation=cv2.INTER_LINEAR) 53 | 54 | pred_disparities_resized.append(pred_disp) 55 | 56 | mask = gt_disp > 0 57 | 58 | gt_depth = width_to_focal[width] * 0.54 / (gt_disp + (1.0 - mask)) 59 | pred_depth = width_to_focal[width] * 0.54 / pred_disp 60 | 61 | gt_depths.append(gt_depth) 62 | pred_depths.append(pred_depth) 63 | return gt_depths, pred_depths, pred_disparities_resized 64 | 65 | 66 | ############################################################################### 67 | ####################### EIGEN 68 | 69 | def read_text_lines(file_path): 70 | f = open(file_path, 'r') 71 | lines = f.readlines() 72 | f.close() 73 | lines = [l.rstrip() for l in lines] 74 | return lines 75 | 76 | def read_file_data(files, data_root): 77 | gt_files = [] 78 | gt_calib = [] 79 | im_sizes = [] 80 | im_files = [] 81 | cams = [] 82 | num_probs = 0 83 | for filename in files: 84 | filename = filename.split()[0] 85 | splits = filename.split('/') 86 | camera_id = np.int32(splits[2][-1:]) # 2 is left, 3 is right 87 | date = splits[0] 88 | im_id = splits[4][:10] 89 | file_root = '{}/{}' 90 | 91 | im = filename 92 | vel = '{}/{}/velodyne_points/data/{}.bin'.format(splits[0], splits[1], im_id) 93 | 94 | if os.path.isfile(data_root + im): 95 | gt_files.append(data_root + vel) 96 | gt_calib.append(data_root + date + '/') 97 | print(data_root + im) 98 | im_sizes.append(cv2.imread(data_root + im).shape[:2]) 99 | im_files.append(data_root + im) 100 | cams.append(2) 101 | else: 102 | num_probs += 1 103 | print('{} missing'.format(data_root + im)) 104 | print(str(num_probs) + ' files missing') 105 | 106 | return gt_files, gt_calib, im_sizes, im_files, cams 107 | 108 | def load_velodyne_points(file_name): 109 | # adapted from https://github.com/hunse/kitti 110 | points = np.fromfile(file_name, dtype=np.float32).reshape(-1, 4) 111 | points[:, 3] = 1.0 # homogeneous 112 | return points 113 | 114 | 115 | def lin_interp(shape, xyd): 116 | # taken from https://github.com/hunse/kitti 117 | m, n = shape 118 | ij, d = xyd[:, 1::-1], xyd[:, 2] 119 | f = LinearNDInterpolator(ij, d, fill_value=0) 120 | J, I = np.meshgrid(np.arange(n), np.arange(m)) 121 | IJ = np.vstack([I.flatten(), J.flatten()]).T 122 | disparity = f(IJ).reshape(shape) 123 | return disparity 124 | 125 | 126 | def read_calib_file(path): 127 | # taken from https://github.com/hunse/kitti 128 | float_chars = set("0123456789.e+- ") 129 | data = {} 130 | with open(path, 'r') as f: 131 | for line in f.readlines(): 132 | key, value = line.split(':', 1) 133 | value = value.strip() 134 | data[key] = value 135 | if float_chars.issuperset(value): 136 | # try to cast to float array 137 | try: 138 | data[key] = np.array(map(float, value.split(' '))) 139 | except ValueError: 140 | # casting error: data[key] already eq. value, so pass 141 | pass 142 | 143 | return data 144 | 145 | 146 | def get_focal_length_baseline(calib_dir, cam): 147 | cam2cam = read_calib_file(calib_dir + 'calib_cam_to_cam.txt') 148 | P2_rect = cam2cam['P_rect_02'].reshape(3,4) 149 | P3_rect = cam2cam['P_rect_03'].reshape(3,4) 150 | 151 | # cam 2 is left of camera 0 -6cm 152 | # cam 3 is to the right +54cm 153 | b2 = P2_rect[0,3] / -P2_rect[0,0] 154 | b3 = P3_rect[0,3] / -P3_rect[0,0] 155 | baseline = b3-b2 156 | 157 | if cam==2: 158 | focal_length = P2_rect[0,0] 159 | elif cam==3: 160 | focal_length = P3_rect[0,0] 161 | 162 | return focal_length, baseline 163 | 164 | 165 | def sub2ind(matrixSize, rowSub, colSub): 166 | m, n = matrixSize 167 | return rowSub * (n-1) + colSub - 1 168 | 169 | def generate_depth_map(calib_dir, velo_file_name, im_shape, cam=2, interp=False, vel_depth=False): 170 | # load calibration files 171 | cam2cam = read_calib_file(calib_dir + 'calib_cam_to_cam.txt') 172 | velo2cam = read_calib_file(calib_dir + 'calib_velo_to_cam.txt') 173 | velo2cam = np.hstack((velo2cam['R'].reshape(3,3), velo2cam['T'][..., np.newaxis])) 174 | velo2cam = np.vstack((velo2cam, np.array([0, 0, 0, 1.0]))) 175 | 176 | # compute projection matrix velodyne->image plane 177 | R_cam2rect = np.eye(4) 178 | R_cam2rect[:3,:3] = cam2cam['R_rect_00'].reshape(3,3) 179 | P_rect = cam2cam['P_rect_0'+str(cam)].reshape(3,4) 180 | P_velo2im = np.dot(np.dot(P_rect, R_cam2rect), velo2cam) 181 | 182 | # load velodyne points and remove all behind image plane (approximation) 183 | # each row of the velodyne data is forward, left, up, reflectance 184 | velo = load_velodyne_points(velo_file_name) 185 | velo = velo[velo[:, 0] >= 0, :] 186 | 187 | # project the points to the camera 188 | velo_pts_im = np.dot(P_velo2im, velo.T).T 189 | velo_pts_im[:, :2] = velo_pts_im[:,:2] / velo_pts_im[:,2][..., np.newaxis] 190 | 191 | if vel_depth: 192 | velo_pts_im[:, 2] = velo[:, 0] 193 | 194 | # check if in bounds 195 | # use minus 1 to get the exact same value as KITTI matlab code 196 | velo_pts_im[:, 0] = np.round(velo_pts_im[:,0]) - 1 197 | velo_pts_im[:, 1] = np.round(velo_pts_im[:,1]) - 1 198 | val_inds = (velo_pts_im[:, 0] >= 0) & (velo_pts_im[:, 1] >= 0) 199 | val_inds = val_inds & (velo_pts_im[:,0] < im_shape[1]) & (velo_pts_im[:,1] < im_shape[0]) 200 | velo_pts_im = velo_pts_im[val_inds, :] 201 | 202 | # project to image 203 | depth = np.zeros((im_shape)) 204 | depth[velo_pts_im[:, 1].astype(np.int), velo_pts_im[:, 0].astype(np.int)] = velo_pts_im[:, 2] 205 | 206 | # find the duplicate points and choose the closest depth 207 | inds = sub2ind(depth.shape, velo_pts_im[:, 1], velo_pts_im[:, 0]) 208 | dupe_inds = [item for item, count in Counter(inds).iteritems() if count > 1] 209 | for dd in dupe_inds: 210 | pts = np.where(inds==dd)[0] 211 | x_loc = int(velo_pts_im[pts[0], 0]) 212 | y_loc = int(velo_pts_im[pts[0], 1]) 213 | depth[y_loc, x_loc] = velo_pts_im[pts, 2].min() 214 | depth[depth<0] = 0 215 | 216 | if interp: 217 | # interpolate the depth map to fill in holes 218 | depth_interp = lin_interp(im_shape, velo_pts_im) 219 | return depth, depth_interp 220 | else: 221 | return depth 222 | 223 | 224 | 225 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | # Code for 2 | # Unsupervised Adversarial Depth Estimation using Cycled Generative Networks 3 | # Andrea Pilzer, Dan Xu, Mihai Puscas, Elisa Ricci, Nicu Sebe 4 | # 5 | # 3DV 2018 Conference, Verona, Italy 6 | # 7 | # parts of the code from https://github.com/mrharicot/monodepth 8 | # 9 | 10 | from __future__ import absolute_import, division, print_function 11 | 12 | # only keep warnings and errors 13 | import os 14 | os.environ['TF_CPP_MIN_LOG_LEVEL']='1' 15 | 16 | import numpy as np 17 | import argparse 18 | import re 19 | import time 20 | import tensorflow as tf 21 | import tensorflow.contrib.slim as slim 22 | import matplotlib.pyplot as plt 23 | 24 | from monodepth_dataloader import * 25 | from average_gradients import * 26 | from model_stereo_depthGAN import * 27 | 28 | 29 | parser = argparse.ArgumentParser(description='Monodepth TensorFlow implementation.') 30 | 31 | parser.add_argument('--mode', type=str, help='train or test', default='train') 32 | parser.add_argument('--model_name', type=str, help='model name', default='stereo_depthGAN') 33 | parser.add_argument('--encoder', type=str, help='type of encoder, resnet50', default='resnet50') 34 | parser.add_argument('--dataset', type=str, help='dataset to train on, kitti, or cityscapes', default='kitti') 35 | parser.add_argument('--data_path', type=str, help='path to the data', required=True) 36 | parser.add_argument('--filenames_file', type=str, help='path to the filenames text file', required=True) 37 | parser.add_argument('--input_height', type=int, help='input height', default=256) 38 | parser.add_argument('--input_width', type=int, help='input width', default=512) 39 | parser.add_argument('--batch_size', type=int, help='batch size', default=8) 40 | parser.add_argument('--num_epochs', type=int, help='number of epochs', default=50) 41 | parser.add_argument('--learning_rate', type=float, help='initial learning rate', default=1e-5) 42 | parser.add_argument('--lr_loss_weight', type=float, help='left-right consistency weight', default=1.0) 43 | parser.add_argument('--do_stereo', help='if set, will train the stereo model', action='store_true') 44 | parser.add_argument('--wrap_mode', type=str, help='bilinear sampler wrap mode, edge or border', default='border') 45 | parser.add_argument('--use_deconv', help='if set, will use transposed convolutions', action='store_true') 46 | parser.add_argument('--num_gpus', type=int, help='number of GPUs to use for training', default=1) 47 | parser.add_argument('--num_threads', type=int, help='number of threads to use for data loading', default=8) 48 | parser.add_argument('--output_directory', type=str, help='output directory for test disparities, if empty outputs to checkpoint folder', default='') 49 | parser.add_argument('--log_directory', type=str, help='directory to save checkpoints and summaries', default='/data1/pilzer/monodepth-GAN/') 50 | parser.add_argument('--checkpoint_path', type=str, help='path to a specific checkpoint to load', default='') 51 | parser.add_argument('--retrain', help='if used with checkpoint_path, will restart training from step zero', action='store_true') 52 | parser.add_argument('--full_summary', help='if set, will keep more data for each summary. Warning: the file can become very large', action='store_true') 53 | parser.add_argument('--train_branch', type=str, help='which branch to train iteratively', default='full') 54 | 55 | args = parser.parse_args() 56 | 57 | def count_text_lines(file_path): 58 | f = open(file_path, 'r') 59 | lines = f.readlines() 60 | f.close() 61 | return len(lines) 62 | 63 | def train(params): 64 | """Training loop.""" 65 | 66 | with tf.Graph().as_default(), tf.device('/cpu:0'): 67 | 68 | global_step = tf.Variable(0, trainable=False) 69 | 70 | # OPTIMIZER 71 | num_training_samples = count_text_lines(args.filenames_file) 72 | 73 | steps_per_epoch = np.ceil(num_training_samples / params.batch_size).astype(np.int32) 74 | num_total_steps = params.num_epochs * steps_per_epoch 75 | start_learning_rate = args.learning_rate 76 | 77 | boundaries = [np.int32((3/5) * num_total_steps), np.int32((4/5) * num_total_steps)] 78 | values = [args.learning_rate, args.learning_rate / 2, args.learning_rate / 4] 79 | learning_rate = tf.train.piecewise_constant(global_step, boundaries, values) 80 | 81 | opt_step = tf.train.AdamOptimizer(learning_rate) 82 | 83 | print("total number of samples: {}".format(num_training_samples)) 84 | print("total number of steps: {}".format(num_total_steps)) 85 | 86 | dataloader = MonodepthDataloader(args.data_path, args.filenames_file, params, args.dataset, args.mode) 87 | left = dataloader.left_image_batch 88 | right = dataloader.right_image_batch 89 | 90 | # split for each gpu 91 | left_splits = tf.split(left, args.num_gpus, 0) 92 | right_splits = tf.split(right, args.num_gpus, 0) 93 | 94 | tower_grads = [] 95 | tower_losses = [] 96 | reuse_variables = None 97 | with tf.variable_scope(tf.get_variable_scope()): 98 | for i in range(args.num_gpus): 99 | print(i) 100 | with tf.device('/gpu:%d' % i): 101 | model = stereo_depthGAN_Model(params, args.train_branch, args.mode, left_splits[i], right_splits[i], reuse_variables, i) 102 | 103 | loss = model.total_loss 104 | tower_losses.append(loss) 105 | 106 | reuse_variables = True 107 | 108 | if args.train_branch == 'a2b': 109 | grads_discrA = opt_step.compute_gradients(model.discr_loss, model.discrA_vars) 110 | new_grads_discrA = [(g*10, v) for g, v in grads_discrA] 111 | grads_gen2b = opt_step.compute_gradients(loss, model.gen2b_vars) 112 | grads_encoder = opt_step.compute_gradients(loss, model.encoder_vars) 113 | 114 | grads = grads_gen2b + grads_encoder + new_grads_discrA 115 | elif args.train_branch == 'b2a': 116 | grads_discrB = opt_step.compute_gradients(model.discr_loss, model.discrB_vars) 117 | new_grads_discrB = [(g*10, v) for g, v in grads_discrB] 118 | grads_gen2a = opt_step.compute_gradients(loss, model.gen2a_vars) 119 | grads_encoder = opt_step.compute_gradients(loss, model.encoder_vars) 120 | 121 | grads = grads_gen2a + grads_encoder + new_grads_discrB 122 | else: 123 | grads_discrA = opt_step.compute_gradients(model.discr_loss, model.discrA_vars) 124 | new_grads_discrA = [(g*10, v) for g, v in grads_discrA] 125 | grads_gen2b = opt_step.compute_gradients(loss, model.gen2b_vars) 126 | grads_discrB = opt_step.compute_gradients(model.discr_loss, model.discrB_vars) 127 | new_grads_discrB = [(g*10, v) for g, v in grads_discrB] 128 | grads_gen2a = opt_step.compute_gradients(loss, model.gen2a_vars) 129 | grads_encoder = opt_step.compute_gradients(loss, model.encoder_vars) 130 | 131 | grads = grads_gen2b + new_grads_discrA + grads_gen2a + new_grads_discrB + grads_encoder 132 | 133 | tower_grads.append(grads) 134 | 135 | grads = average_gradients(tower_grads) 136 | 137 | apply_gradient_op = opt_step.apply_gradients(grads, global_step=global_step) 138 | 139 | total_loss = tf.reduce_mean(tower_losses) 140 | 141 | tf.summary.scalar('learning_rate', learning_rate, ['model_0']) 142 | tf.summary.scalar('total_loss', total_loss, ['model_0']) 143 | summary_op = tf.summary.merge_all('model_0') 144 | 145 | 146 | # SESSION 147 | config = tf.ConfigProto(allow_soft_placement=True) 148 | config.gpu_options.allow_growth = True 149 | sess = tf.Session(config=config) 150 | 151 | # SAVER 152 | summary_writer = tf.summary.FileWriter(args.log_directory + '/' + args.model_name, sess.graph) 153 | #train_saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=0) 154 | train_saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=0) 155 | 156 | # COUNT PARAMS 157 | total_num_parameters = 0 158 | for variable in tf.trainable_variables(): 159 | print(variable) 160 | total_num_parameters += np.array(variable.get_shape().as_list()).prod() 161 | print("number of trainable parameters: {}".format(total_num_parameters)) 162 | 163 | # INIT 164 | sess.run(tf.global_variables_initializer()) 165 | sess.run(tf.local_variables_initializer()) 166 | coordinator = tf.train.Coordinator() 167 | threads = tf.train.start_queue_runners(sess=sess, coord=coordinator) 168 | 169 | # LOAD CHECKPOINT IF SET 170 | if args.checkpoint_path != '': 171 | if args.train_branch == 'a2b': 172 | vlist = [] 173 | temp_saver = tf.train.Saver(var_list=vlist) 174 | temp_saver.restore(sess, args.checkpoint_path) 175 | 176 | if args.retrain: 177 | sess.run(global_step.assign(0)) 178 | elif args.train_branch == 'b2a': 179 | vars_in_checkpoint = tf.contrib.framework.list_variables(args.checkpoint_path) 180 | all_variables = tf.get_collection_ref(tf.GraphKeys.GLOBAL_VARIABLES) 181 | vlist = [] 182 | 183 | for v in all_variables: 184 | if '2b' in v.name: 185 | if 'Adam' not in v.name: 186 | vlist.append(v) 187 | if 'enc' in v.name: 188 | if 'Adam' not in v.name: 189 | vlist.append(v) 190 | if 'discriminatorB' in v.name: 191 | if 'Adam' not in v.name: 192 | vlist.append(v) 193 | temp_saver = tf.train.Saver(var_list=vlist) 194 | temp_saver.restore(sess, args.checkpoint_path) 195 | 196 | if args.retrain: 197 | sess.run(global_step.assign(0)) 198 | else: 199 | vars_in_checkpoint = tf.contrib.framework.list_variables(args.checkpoint_path) 200 | all_variables = tf.get_collection_ref(tf.GraphKeys.GLOBAL_VARIABLES) 201 | vlist = [] 202 | 203 | for v in all_variables: 204 | if '2a' in v.name: 205 | if 'Adam' not in v.name: 206 | vlist.append(v) 207 | if '2b' in v.name: 208 | if 'Adam' not in v.name: 209 | vlist.append(v) 210 | if 'enc' in v.name: 211 | if 'Adam' not in v.name: 212 | vlist.append(v) 213 | if 'discriminator' in v.name: 214 | if 'Adam' not in v.name: 215 | vlist.append(v) 216 | temp_saver = tf.train.Saver(var_list=vlist) 217 | temp_saver.restore(sess, args.checkpoint_path) 218 | 219 | if args.retrain: 220 | sess.run(global_step.assign(0)) 221 | 222 | # GO! 223 | start_step = global_step.eval(session=sess) 224 | start_time = time.time() 225 | for step in range(start_step, num_total_steps): 226 | before_op_time = time.time() 227 | _, loss_value = sess.run([apply_gradient_op, total_loss]) 228 | duration = time.time() - before_op_time 229 | if step and step % 200 == 0: 230 | examples_per_sec = params.batch_size / duration 231 | time_sofar = (time.time() - start_time) / 3600 232 | training_time_left = (num_total_steps / step - 1.0) * time_sofar 233 | print_string = 'batch {:>6} | examples/s: {:4.2f} | loss: {:.5f} | time elapsed: {:.2f}h | time left: {:.2f}h' 234 | print(print_string.format(step, examples_per_sec, loss_value, time_sofar, training_time_left)) 235 | summary_str = sess.run(summary_op) 236 | summary_writer.add_summary(summary_str, global_step=step) 237 | if step and step % 2500 == 0: 238 | train_saver.save(sess, args.log_directory + '/' + args.model_name + '/model', global_step=step) 239 | 240 | train_saver.save(sess, args.log_directory + '/' + args.model_name + '/model', global_step=num_total_steps) 241 | 242 | def test(params): 243 | """Test function.""" 244 | global_step = tf.Variable(0, trainable=False) 245 | 246 | dataloader = MonodepthDataloader(args.data_path, args.filenames_file, params, args.dataset, args.mode) 247 | left = dataloader.left_image_batch 248 | right = dataloader.right_image_batch 249 | 250 | # split for each gpu 251 | #left_splits = tf.split(left, args.num_gpus, 0) 252 | #right_splits = tf.split(right, args.num_gpus, 0) 253 | 254 | reuse_variables = None 255 | with tf.variable_scope(tf.get_variable_scope()): 256 | for i in range(args.num_gpus): 257 | with tf.device('/gpu:%d' % i): 258 | model = stereo_depthGAN_Model(params, args.train_branch, args.mode, left, right, reuse_variables, i) 259 | 260 | reuse_variables = True 261 | 262 | # SESSION 263 | config = tf.ConfigProto(allow_soft_placement=True) 264 | config.gpu_options.allow_growth = True 265 | sess = tf.Session(config=config) 266 | 267 | # SAVER 268 | train_saver = tf.train.Saver(tf.trainable_variables()) 269 | #train_saver = tf.train.Saver() 270 | 271 | # INIT 272 | sess.run(tf.global_variables_initializer()) 273 | sess.run(tf.local_variables_initializer()) 274 | coordinator = tf.train.Coordinator() 275 | threads = tf.train.start_queue_runners(sess=sess, coord=coordinator) 276 | 277 | # RESTORE 278 | if args.checkpoint_path == '': 279 | restore_path = tf.train.latest_checkpoint(args.log_directory + '/' + args.model_name) 280 | else: 281 | restore_path = args.checkpoint_path 282 | train_saver.restore(sess, restore_path) 283 | 284 | num_test_samples = count_text_lines(args.filenames_file) 285 | 286 | print('now testing {} files'.format(num_test_samples)) 287 | disparities = np.zeros((num_test_samples, params.height, params.width), dtype=np.float32) 288 | os.mkdir(args.log_directory + args.model_name) 289 | cnt = 0 290 | while cnt < num_test_samples: 291 | 292 | disp = sess.run(model.disp_out) 293 | #disp = sess.run(model.disp_right_est[0]) 294 | for d in disp: 295 | if cnt < num_test_samples: 296 | disparities[cnt] = d.squeeze() 297 | cmap = plt.cm.plasma 298 | norm = plt.Normalize(vmin=disparities[cnt].min(), vmax=disparities[cnt].max()) 299 | images = cmap(norm(disparities[cnt])) 300 | #plt.imshow(images) 301 | #plt.show() 302 | plt.imsave(args.log_directory + args.model_name + '/img_' + str(cnt) + '.png' ,images) 303 | cnt += 1 304 | print('done.') 305 | 306 | print('writing disparities.') 307 | if args.output_directory == '': 308 | #os.mkdir(args.checkpoint_path) 309 | output_directory = args.checkpoint_path 310 | else: 311 | output_directory = args.output_directory 312 | np.save(output_directory + '/disparities.npy', disparities) 313 | print('done.') 314 | 315 | def main(_): 316 | 317 | params = monodepth_parameters( 318 | encoder=args.encoder, 319 | height=args.input_height, 320 | width=args.input_width, 321 | batch_size=args.batch_size, 322 | num_threads=args.num_threads, 323 | num_epochs=args.num_epochs, 324 | do_stereo=args.do_stereo, 325 | wrap_mode=args.wrap_mode, 326 | use_deconv=args.use_deconv, 327 | lr_loss_weight=args.lr_loss_weight, 328 | full_summary=args.full_summary, 329 | num_gpus=args.num_gpus) 330 | 331 | if args.mode == 'train': 332 | train(params) 333 | elif args.mode == 'test': 334 | test(params) 335 | 336 | if __name__ == '__main__': 337 | tf.app.run() 338 | -------------------------------------------------------------------------------- /model_stereo_depthGAN.py: -------------------------------------------------------------------------------- 1 | # Code for 2 | # Unsupervised Adversarial Depth Estimation using Cycled Generative Networks 3 | # Andrea Pilzer, Dan Xu, Mihai Puscas, Elisa Ricci, Nicu Sebe 4 | # 5 | # 3DV 2018 Conference, Verona, Italy 6 | # 7 | # parts of the code from https://github.com/mrharicot/monodepth 8 | # 9 | 10 | from __future__ import absolute_import, division, print_function 11 | from collections import namedtuple 12 | 13 | import numpy as np 14 | import tensorflow as tf 15 | import tensorflow.contrib.slim as slim 16 | 17 | from bilinear_sampler import * 18 | from module import * 19 | 20 | monodepth_parameters = namedtuple('parameters', 21 | 'encoder, ' 22 | 'height, width, ' 23 | 'batch_size, ' 24 | 'num_threads, ' 25 | 'num_epochs, ' 26 | 'do_stereo, ' 27 | 'wrap_mode, ' 28 | 'use_deconv, ' 29 | 'lr_loss_weight, ' 30 | 'full_summary, ' 31 | 'num_gpus') 32 | 33 | class stereo_depthGAN_Model(object): 34 | """unsupervised stereo depthGAN model""" 35 | 36 | def __init__(self, params, branch, mode, left, right, reuse_variables=None, model_index=0): 37 | self.params = params 38 | self.branch = branch 39 | self.mode = mode 40 | self.left = left 41 | self.right = right 42 | self.model_collection = ['model_' + str(model_index)] 43 | 44 | self.batch_size = params.batch_size/params.num_gpus 45 | self.size_W = params.width 46 | self.size_H = params.height 47 | self.input_c_dim = 3 48 | self.output_c_dim = 1 49 | self.identity = tf.Variable(tf.ones([self.batch_size, self.size_H, self.size_W, 1]), trainable=False) 50 | 51 | self.reuse_variables = reuse_variables 52 | self.reuse = reuse_variables 53 | self.discriminator = self.discr 54 | self.encoder = self.build_resnet50_enc 55 | self.decoder = self.build_resnet50_dec 56 | self.fusion = self.fusion_func 57 | 58 | self.criterionGAN = mae_criterion 59 | 60 | OPTIONS = namedtuple('OPTIONS', 'batch_size image_size \ 61 | gf_dim df_dim output_c_dim is_training') 62 | self.options = OPTIONS._make((params.batch_size, params.height, 63 | 64, 64, self.output_c_dim, 64 | mode == 'train')) 65 | 66 | self.build() 67 | #self.build_outputs() 68 | 69 | if self.mode == 'test': 70 | self.disp_out = self.disp_output() 71 | return 72 | 73 | self.build_losses() 74 | self.build_summaries() 75 | 76 | def gradient_x(self, img): 77 | gx = img[:,:,:-1,:] - img[:,:,1:,:] 78 | return gx 79 | 80 | def gradient_y(self, img): 81 | gy = img[:,:-1,:,:] - img[:,1:,:,:] 82 | return gy 83 | 84 | def upsample_nn(self, x, ratio): 85 | s = x.get_shape().as_list() 86 | h = s[1] 87 | w = s[2] 88 | return tf.image.resize_nearest_neighbor(x, [h * ratio, w * ratio]) 89 | 90 | def scale_pyramid(self, img, num_scales): 91 | scaled_imgs = [img] 92 | s = tf.shape(img) 93 | h = s[1] 94 | w = s[2] 95 | for i in range(num_scales - 1): 96 | ratio = 2 ** (i + 1) 97 | nh = h // ratio 98 | nw = w // ratio 99 | scaled_imgs.append(tf.image.resize_area(img, [nh, nw])) 100 | return scaled_imgs 101 | 102 | def generate_image_left(self, img, disp): 103 | return bilinear_sampler_1d_h(img, -disp) 104 | 105 | def generate_image_right(self, img, disp): 106 | return bilinear_sampler_1d_h(img, disp) 107 | 108 | def get_disp_original(self, x): 109 | disp = 0.3 * self.conv(x, 2, 3, 1, tf.nn.sigmoid) 110 | return disp 111 | 112 | def get_disp(self, x): 113 | disp = 0.3 * self.conv(x, 1, 3, 1, tf.nn.sigmoid) 114 | return disp 115 | 116 | def conv(self, x, num_out_layers, kernel_size, stride, activation_fn=tf.nn.elu): 117 | p = np.floor((kernel_size - 1) / 2).astype(np.int32) 118 | p_x = tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]]) 119 | return slim.conv2d(p_x, num_out_layers, kernel_size, stride, 'VALID', activation_fn=activation_fn) 120 | 121 | def conv_block(self, x, num_out_layers, kernel_size): 122 | conv1 = self.conv(x, num_out_layers, kernel_size, 1) 123 | conv2 = self.conv(conv1, num_out_layers, kernel_size, 2) 124 | return conv2 125 | 126 | def maxpool(self, x, kernel_size): 127 | p = np.floor((kernel_size - 1) / 2).astype(np.int32) 128 | p_x = tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]]) 129 | return slim.max_pool2d(p_x, kernel_size) 130 | 131 | def resconv(self, x, num_layers, stride): 132 | do_proj = tf.shape(x)[3] != num_layers or stride == 2 133 | shortcut = [] 134 | conv1 = self.conv(x, num_layers, 1, 1) 135 | conv2 = self.conv(conv1, num_layers, 3, stride) 136 | conv3 = self.conv(conv2, 4 * num_layers, 1, 1, None) 137 | if do_proj: 138 | shortcut = self.conv(x, 4 * num_layers, 1, stride, None) 139 | else: 140 | shortcut = x 141 | return tf.nn.elu(conv3 + shortcut) 142 | 143 | def resblock(self, x, num_layers, num_blocks): 144 | out = x 145 | for i in range(num_blocks - 1): 146 | out = self.resconv(out, num_layers, 1) 147 | out = self.resconv(out, num_layers, 2) 148 | return out 149 | 150 | def upconv(self, x, num_out_layers, kernel_size, scale): 151 | upsample = self.upsample_nn(x, scale) 152 | conv = self.conv(upsample, num_out_layers, kernel_size, 1) 153 | return conv 154 | 155 | def deconv(self, x, num_out_layers, kernel_size, scale): 156 | p_x = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) 157 | conv = slim.conv2d_transpose(p_x, num_out_layers, kernel_size, scale, 'SAME') 158 | return conv[:,3:-1,3:-1,:] 159 | 160 | def build_resnet50_enc(self, net_input, name, direction='', reuse=False): 161 | with tf.variable_scope(name): 162 | if reuse: 163 | tf.get_variable_scope().reuse_variables() 164 | elif reuse == False and self.reuse == True: 165 | tf.get_variable_scope().reuse_variables() 166 | elif reuse == True and self.reuse == False: 167 | tf.get_variable_scope().reuse_variables() 168 | elif reuse == False and self.reuse == False: 169 | assert tf.get_variable_scope().reuse is False 170 | #set convenience functions 171 | conv = self.conv 172 | if self.params.use_deconv: 173 | upconv = self.deconv 174 | else: 175 | upconv = self.upconv 176 | 177 | with tf.variable_scope('encoder'): 178 | conv1 = conv(net_input, 64, 7, 2) # H/2 - 64D 179 | pool1 = self.maxpool(conv1, 3) # H/4 - 64D 180 | conv2 = self.resblock(pool1, 64, 3) # H/8 - 256D 181 | conv3 = self.resblock(conv2, 128, 4) # H/16 - 512D 182 | conv4 = self.resblock(conv3, 256, 6) # H/32 - 1024D 183 | conv5 = self.resblock(conv4, 512, 3) # H/64 - 2048D 184 | 185 | with tf.variable_scope('skips'): 186 | skip1 = conv1 187 | skip2 = pool1 188 | skip3 = conv2 189 | skip4 = conv3 190 | skip5 = conv4 191 | 192 | return skip1, skip2, skip3, skip4, skip5, conv5 193 | 194 | def build_resnet50_dec(self, skip1, skip2, skip3, skip4, skip5, conv5, name, direction='', reuse=False): 195 | with tf.variable_scope(name): 196 | if reuse: 197 | tf.get_variable_scope().reuse_variables() 198 | elif reuse == False and self.reuse == True: 199 | tf.get_variable_scope().reuse_variables() 200 | elif reuse == True and self.reuse == False: 201 | tf.get_variable_scope().reuse_variables() 202 | elif reuse == False and self.reuse == False: 203 | assert tf.get_variable_scope().reuse is False 204 | #set convenience functions 205 | conv = self.conv 206 | if self.params.use_deconv: 207 | upconv = self.deconv 208 | else: 209 | upconv = self.upconv 210 | # DECODING 211 | with tf.variable_scope('decoder'): 212 | upconv6 = upconv(conv5, 512, 3, 2) #H/32 213 | concat6 = tf.concat([upconv6, skip5], 3) 214 | iconv6 = conv(concat6, 512, 3, 1) 215 | 216 | upconv5 = upconv(iconv6, 256, 3, 2) #H/16 217 | concat5 = tf.concat([upconv5, skip4], 3) 218 | iconv5 = conv(concat5, 256, 3, 1) 219 | 220 | upconv4 = upconv(iconv5, 128, 3, 2) #H/8 221 | concat4 = tf.concat([upconv4, skip3], 3) 222 | iconv4 = conv(concat4, 128, 3, 1) 223 | disp4 = self.get_disp(iconv4) 224 | udisp4 = self.upsample_nn(disp4, 2) 225 | 226 | upconv3 = upconv(iconv4, 64, 3, 2) #H/4 227 | concat3 = tf.concat([upconv3, skip2, udisp4], 3) 228 | iconv3 = conv(concat3, 64, 3, 1) 229 | disp3 = self.get_disp(iconv3) 230 | udisp3 = self.upsample_nn(disp3, 2) 231 | 232 | upconv2 = upconv(iconv3, 32, 3, 2) #H/2 233 | concat2 = tf.concat([upconv2, skip1, udisp3], 3) 234 | iconv2 = conv(concat2, 32, 3, 1) 235 | disp2 = self.get_disp(iconv2) 236 | udisp2 = self.upsample_nn(disp2, 2) 237 | 238 | upconv1 = upconv(iconv2, 16, 3, 2) #H 239 | concat1 = tf.concat([upconv1, udisp2], 3) 240 | iconv1 = conv(concat1, 16, 3, 1) 241 | disp1 = self.get_disp(iconv1) 242 | 243 | return disp4, disp3, disp2, disp1 244 | 245 | def discr(self,image, options, reuse=False, name="discriminator"): 246 | with tf.variable_scope(name): 247 | if reuse: 248 | tf.get_variable_scope().reuse_variables() 249 | elif reuse == False and self.reuse == True: 250 | tf.get_variable_scope().reuse_variables() 251 | elif reuse == True and self.reuse == False: 252 | tf.get_variable_scope().reuse_variables() 253 | elif reuse == False and self.reuse == False: 254 | assert tf.get_variable_scope().reuse is False 255 | 256 | h0 = lrelu(conv2d(image, options.df_dim, name='d_h0_conv')) 257 | # h0 is (128 x 128 x self.df_dim) 258 | h1 = lrelu(instance_norm(conv2d(h0, options.df_dim*2, name='d_h1_conv'), 'd_bn1')) 259 | # h1 is (64 x 64 x self.df_dim*2) 260 | h2 = lrelu(instance_norm(conv2d(h1, options.df_dim*4, name='d_h2_conv'), 'd_bn2')) 261 | # h2 is (32x 32 x self.df_dim*4) 262 | h3 = lrelu(instance_norm(conv2d(h2, options.df_dim*8, s=1, name='d_h3_conv'), 'd_bn3')) 263 | # h3 is (32 x 32 x self.df_dim*8) 264 | h4 = conv2d(h3, 1, s=1, name='d_h3_pred') 265 | # h4 is (32 x 32 x 1) 266 | return h4 267 | 268 | def fusion_func(self, net_input1, net_input2, name, reuse=False): 269 | with tf.variable_scope(name): 270 | conv = self.conv 271 | if reuse: 272 | tf.get_variable_scope().reuse_variables() 273 | elif reuse == False and self.reuse == True: 274 | tf.get_variable_scope().reuse_variables() 275 | elif reuse == True and self.reuse == False: 276 | tf.get_variable_scope().reuse_variables() 277 | elif reuse == False and self.reuse == False: 278 | assert tf.get_variable_scope().reuse is False 279 | if reuse: 280 | tf.get_variable_scope().reuse_variables() 281 | input_fusion = tf.concat([net_input1, net_input2], 3) 282 | conv_fusion = conv(input_fusion, 1, 1, 1, tf.nn.relu) 283 | return conv_fusion 284 | 285 | def build(self): 286 | with slim.arg_scope([slim.conv2d, slim.conv2d_transpose], activation_fn=tf.nn.elu): 287 | with tf.variable_scope('model', reuse=self.reuse_variables): 288 | 289 | self.left_pyramid = self.scale_pyramid(self.left, 4) 290 | self.right_pyramid = self.scale_pyramid(self.right, 4) 291 | #right 2 left - forward cycle 292 | self.skip1_b2a, self.skip2_b2a, self.skip3_b2a, self.skip4_b2a, self.skip5_b2a, self.conv5_b2a = self.encoder(self.right, direction='right', name='enc', reuse=False) 293 | self.skip1_a2a, self.skip2_a2a, self.skip3_a2a, self.skip4_a2a, self.skip5_a2a, self.conv5_a2a = self.encoder(self.left, direction='left_implicit', name='enc', reuse=True) 294 | 295 | self.disp4_b2a, self.disp3_b2a, self.disp2_b2a, self.disp1_b2a = self.decoder(self.skip1_b2a, self.skip2_b2a, self.skip3_b2a, self.skip4_b2a, self.skip5_b2a, self.conv5_b2a, direction='right', name='decb2a', reuse=False) 296 | self.disp4_a2a, self.disp3_a2a, self.disp2_a2a, self.disp1_a2a = self.decoder(self.skip1_a2a, self.skip2_a2a, self.skip3_a2a, self.skip4_a2a, self.skip5_a2a, self.conv5_a2a, direction='left_implicit', name='deca2a', reuse=False) 297 | 298 | with tf.variable_scope('disparities'): 299 | self.disp1_l = self.fusion(self.disp1_b2a, self.disp1_a2a, name='fusion2a', reuse=False) 300 | self.disp2_l = self.fusion(self.disp2_b2a, self.disp2_a2a, name='fusion2a', reuse=True) 301 | self.disp3_l = self.fusion(self.disp3_b2a, self.disp3_a2a, name='fusion2a', reuse=True) 302 | self.disp4_l = self.fusion(self.disp4_b2a, self.disp4_a2a, name='fusion2a', reuse=True) 303 | self.disp_left_est = [self.disp1_l, self.disp2_l, self.disp3_l, self.disp4_l] 304 | 305 | # GENERATE IMAGES 306 | with tf.variable_scope('images'): 307 | self.left_est = [self.generate_image_left(self.right_pyramid[i], self.disp_left_est[i]) for i in range(4)] 308 | self.left_est[0].set_shape([self.batch_size, self.size_H, self.size_W, 3]) 309 | 310 | with tf.variable_scope('model'): 311 | #left 2 right - backward cycle 312 | self.skip1_a2b, self.skip2_a2b, self.skip3_a2b, self.skip4_a2b, self.skip5_a2b, self.conv5_a2b = self.encoder(self.left_est[0], direction='left', name='enc', reuse=True) 313 | self.skip1_b2b, self.skip2_b2b, self.skip3_b2b, self.skip4_b2b, self.skip5_b2b, self.conv5_b2b = self.encoder(self.right, direction='right_implicit', name='enc', reuse=True) 314 | 315 | self.disp4_a2b, self.disp3_a2b, self.disp2_a2b, self.disp1_a2b = self.decoder(self.skip1_a2b, self.skip2_a2b, self.skip3_a2b, self.skip4_a2b, self.skip5_a2b, self.conv5_a2b, direction='right', name='deca2b', reuse=False) 316 | self.disp4_b2b, self.disp3_b2b, self.disp2_b2b, self.disp1_b2b = self.decoder(self.skip1_b2b, self.skip2_b2b, self.skip3_b2b, self.skip4_b2b, self.skip5_b2b, self.conv5_b2b, direction='left_implicit', name='decb2b', reuse=False) 317 | 318 | with tf.variable_scope('disparities'): 319 | self.disp1_r = self.fusion(self.disp1_a2b, self.disp1_b2b, name='fusion2b', reuse=False) 320 | self.disp2_r = self.fusion(self.disp2_a2b, self.disp2_b2b, name='fusion2b', reuse=True) 321 | self.disp3_r = self.fusion(self.disp3_a2b, self.disp3_b2b, name='fusion2b', reuse=True) 322 | self.disp4_r = self.fusion(self.disp4_a2b, self.disp4_b2b, name='fusion2b', reuse=True) 323 | self.disp_right_est = [self.disp1_r, self.disp2_r, self.disp3_r, self.disp4_r] 324 | 325 | with tf.variable_scope('images'): 326 | self.right_est = [self.generate_image_right(self.left_pyramid[i], self.disp_right_est[i]) for i in range(4)] 327 | self.right_est[0].set_shape([self.batch_size, self.size_H, self.size_W, 3]) 328 | 329 | with tf.variable_scope('model'): 330 | self.D_left_real = self.discriminator(self.left_pyramid[0], self.options, reuse=False, name="discriminatorB") 331 | self.D_left_fake = self.discriminator(self.left_est[0], self.options, reuse=True, name="discriminatorB") 332 | 333 | self.D_right_real = self.discriminator(self.right_pyramid[0], self.options, reuse=False, name="discriminatorA") 334 | self.D_right_fake = self.discriminator(self.right_est[0], self.options, reuse=True, name="discriminatorA") 335 | 336 | # LR CONSISTENCY 337 | with tf.variable_scope('left-right'): 338 | self.right_to_left_disp = [self.generate_image_left(self.disp_right_est[i], self.disp_left_est[i]) for i in range(4)] 339 | self.left_to_right_disp = [self.generate_image_right(self.disp_left_est[i], self.disp_right_est[i]) for i in range(4)] 340 | 341 | t_vars = tf.trainable_variables() 342 | self.discrA_vars = [var for var in t_vars if 'discriminatorA' in var.name] 343 | self.discrB_vars = [var for var in t_vars if 'discriminatorB' in var.name] 344 | self.encoder_vars = [var for var in t_vars if 'enc' in var.name] 345 | self.gen2b_vars = [var for var in t_vars if '2b' in var.name] 346 | self.gen2a_vars = [var for var in t_vars if '2a' in var.name] 347 | 348 | def disp_output(self): 349 | disp_out = tf.reduce_mean(tf.concat([self.disp_left_est[0], self.right_to_left_disp[0]], 3), 3, keep_dims=True) 350 | return disp_out 351 | 352 | def build_losses(self): 353 | with tf.variable_scope('losses', reuse=self.reuse_variables): 354 | # IMAGE RECONSTRUCTION 355 | # L1 (identity) 356 | self.l1_left = [tf.abs(self.left_est[i] - self.left_pyramid[i]) for i in range(4)] 357 | self.l1_reconstruction_loss_left = [tf.reduce_mean(l) for l in self.l1_left] 358 | self.l1_right = [tf.abs(self.right_est[i] - self.right_pyramid[i]) for i in range(4)] 359 | self.l1_reconstruction_loss_right = [tf.reduce_mean(l) for l in self.l1_right] 360 | 361 | # L1 (cycle) 362 | self.l1_cycle_b = [tf.abs(self.right_est[i] - self.right_pyramid[i]) for i in range(4)] 363 | self.l1_cycle_backward = [tf.reduce_mean(l) for l in self.l1_cycle_b] 364 | if self.branch == 'a2b': 365 | self.cycle_loss = tf.add_n(self.l1_cycle_backward) 366 | elif self.branch == 'b2a': 367 | self.cycle_loss = 0 368 | else: 369 | self.cycle_loss = tf.add_n(self.l1_cycle_backward) 370 | 371 | #SUM 372 | self.image_loss_right = [self.l1_reconstruction_loss_right[i] for i in range(4)] 373 | self.image_loss_left = [self.l1_reconstruction_loss_left[i] for i in range(4)] 374 | #self.image_loss = tf.add_n(self.image_loss_left + self.image_loss_right) 375 | if self.branch == 'b2a': 376 | self.image_loss = tf.add_n(self.image_loss_left) 377 | elif self.branch == 'a2b': 378 | self.image_loss = tf.add_n(self.image_loss_right) 379 | else: 380 | self.image_loss = tf.add_n(self.image_loss_left + self.image_loss_right) 381 | 382 | # DISCRIMINATOR 383 | # left 384 | self.d_loss_left_real = self.criterionGAN(self.D_left_real, tf.ones_like(self.D_left_real)) 385 | self.d_loss_left_fake = self.criterionGAN(self.D_left_fake, tf.zeros_like(self.D_left_fake)) 386 | self.d_loss_left = (self.d_loss_left_real + self.d_loss_left_fake) / 2 387 | # right 388 | self.d_loss_right_real = self.criterionGAN(self.D_right_real, tf.ones_like(self.D_right_real)) 389 | self.d_loss_right_fake = self.criterionGAN(self.D_right_fake, tf.zeros_like(self.D_right_fake)) 390 | self.d_loss_right = (self.d_loss_right_real + self.d_loss_right_fake) / 2 391 | if self.branch == 'b2a': 392 | self.d_loss = self.d_loss_left 393 | self.d_loss_fake = self.d_loss_left_fake 394 | elif self.branch == 'a2b': 395 | self.d_loss = self.d_loss_right 396 | self.d_loss_fake = self.d_loss_right_fake 397 | else: 398 | self.d_loss = self.d_loss_left + self.d_loss_right 399 | self.d_loss_fake = self.d_loss_left_fake + self.d_loss_right_fake 400 | 401 | # LR CONSISTENCY 402 | self.lr_left_loss = [tf.reduce_mean(tf.abs(self.right_to_left_disp[i] - self.disp_left_est[i])) for i in range(4)] 403 | self.lr_right_loss = [tf.reduce_mean(tf.abs(self.left_to_right_disp[i] - self.disp_left_est[i])) for i in range(4)] 404 | if self.branch == 'b2a': 405 | self.lr_loss = 0 406 | elif self.branch == 'a2b': 407 | self.lr_loss = tf.add_n(self.lr_right_loss) 408 | else: 409 | self.lr_loss = tf.add_n(self.lr_right_loss +self.lr_left_loss) 410 | 411 | # # TOTAL LOSS 412 | self.total_loss = self.image_loss + 0.1 * self.cycle_loss - 0.0001 * self.d_loss_fake + 0.1 * self.lr_loss # + self.params.disp_gradient_loss_weight * self.disp_gradient_loss + self.params.lr_loss_weight * self.lr_loss 413 | self.discr_loss = 0.0001 * self.d_loss 414 | 415 | def build_summaries(self): 416 | # SUMMARIES 417 | with tf.device('/cpu:0'): 418 | 419 | for i in range(4): 420 | tf.summary.scalar('l1_loss_' + str(i), self.l1_reconstruction_loss_left[i] + self.l1_reconstruction_loss_right[i], collections=self.model_collection) 421 | tf.summary.scalar('image_loss_' + str(i), self.image_loss_left[i] + self.image_loss_right[i], collections=self.model_collection) 422 | tf.summary.image('disp_left_est_' + str(i), self.disp_left_est[i] , max_outputs=4, collections=self.model_collection) 423 | tf.summary.image('disp_right_est' + str(i), self.disp_right_est[i], max_outputs=4, collections=self.model_collection) 424 | tf.summary.histogram('disp_left_est_' + str(i), self.disp_left_est[i], collections=self.model_collection) 425 | tf.summary.histogram('disp_right_est_' + str(i), self.disp_right_est[i], collections=self.model_collection) 426 | if self.params.full_summary: 427 | tf.summary.image('left_est_' + str(i), self.left_est[i], max_outputs=4, collections=self.model_collection) 428 | tf.summary.image('right_est_' + str(i), self.right_est[i], max_outputs=4, collections=self.model_collection) 429 | 430 | tf.summary.scalar('d_loss', self.d_loss, collections=self.model_collection) 431 | if self.params.full_summary: 432 | tf.summary.image('left', self.left, max_outputs=4, collections=self.model_collection) 433 | tf.summary.image('right', self.right, max_outputs=4, collections=self.model_collection) 434 | -------------------------------------------------------------------------------- /utils/filenames/cityscapes_val_files.txt: -------------------------------------------------------------------------------- 1 | frankfurt/frankfurt_000000_000294_leftImg8bit.jpg frankfurt/frankfurt_000000_000294_rightImg8bit.jpg 2 | frankfurt/frankfurt_000000_000576_leftImg8bit.jpg frankfurt/frankfurt_000000_000576_rightImg8bit.jpg 3 | frankfurt/frankfurt_000000_001016_leftImg8bit.jpg frankfurt/frankfurt_000000_001016_rightImg8bit.jpg 4 | frankfurt/frankfurt_000000_001236_leftImg8bit.jpg frankfurt/frankfurt_000000_001236_rightImg8bit.jpg 5 | frankfurt/frankfurt_000000_001751_leftImg8bit.jpg frankfurt/frankfurt_000000_001751_rightImg8bit.jpg 6 | frankfurt/frankfurt_000000_002196_leftImg8bit.jpg frankfurt/frankfurt_000000_002196_rightImg8bit.jpg 7 | 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