Dean commited on
Commit
3c0c5aa
1 Parent(s): 9cd8f4a

Finished data import and processing setup, bug in training step

Browse files
.dvc/.gitignore ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ /config.local
2
+ /tmp
3
+ /cache
.dvc/config ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ [core]
2
+ analytics = false
3
+ ['remote "dvc-remote"']
4
+ url = s3://dagshub-savta-depth
.dvc/plots/confusion.json ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "$schema": "https://vega.github.io/schema/vega-lite/v4.json",
3
+ "data": {
4
+ "values": "<DVC_METRIC_DATA>"
5
+ },
6
+ "title": "<DVC_METRIC_TITLE>",
7
+ "mark": "rect",
8
+ "encoding": {
9
+ "x": {
10
+ "field": "<DVC_METRIC_X>",
11
+ "type": "nominal",
12
+ "sort": "ascending",
13
+ "title": "<DVC_METRIC_X_LABEL>"
14
+ },
15
+ "y": {
16
+ "field": "<DVC_METRIC_Y>",
17
+ "type": "nominal",
18
+ "sort": "ascending",
19
+ "title": "<DVC_METRIC_Y_LABEL>"
20
+ },
21
+ "color": {
22
+ "aggregate": "count",
23
+ "type": "quantitative"
24
+ },
25
+ "facet": {
26
+ "field": "rev",
27
+ "type": "nominal"
28
+ }
29
+ }
30
+ }
.dvc/plots/default.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "$schema": "https://vega.github.io/schema/vega-lite/v4.json",
3
+ "data": {
4
+ "values": "<DVC_METRIC_DATA>"
5
+ },
6
+ "title": "<DVC_METRIC_TITLE>",
7
+ "mark": {
8
+ "type": "line"
9
+ },
10
+ "encoding": {
11
+ "x": {
12
+ "field": "<DVC_METRIC_X>",
13
+ "type": "quantitative",
14
+ "title": "<DVC_METRIC_X_LABEL>"
15
+ },
16
+ "y": {
17
+ "field": "<DVC_METRIC_Y>",
18
+ "type": "quantitative",
19
+ "title": "<DVC_METRIC_Y_LABEL>",
20
+ "scale": {
21
+ "zero": false
22
+ }
23
+ },
24
+ "color": {
25
+ "field": "rev",
26
+ "type": "nominal"
27
+ }
28
+ }
29
+ }
.dvc/plots/scatter.json ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "$schema": "https://vega.github.io/schema/vega-lite/v4.json",
3
+ "data": {
4
+ "values": "<DVC_METRIC_DATA>"
5
+ },
6
+ "title": "<DVC_METRIC_TITLE>",
7
+ "mark": "point",
8
+ "encoding": {
9
+ "x": {
10
+ "field": "<DVC_METRIC_X>",
11
+ "type": "quantitative",
12
+ "title": "<DVC_METRIC_X_LABEL>"
13
+ },
14
+ "y": {
15
+ "field": "<DVC_METRIC_Y>",
16
+ "type": "quantitative",
17
+ "title": "<DVC_METRIC_Y_LABEL>",
18
+ "scale": {
19
+ "zero": false
20
+ }
21
+ },
22
+ "color": {
23
+ "field": "rev",
24
+ "type": "nominal"
25
+ }
26
+ }
27
+ }
.dvc/plots/smooth.json ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "$schema": "https://vega.github.io/schema/vega-lite/v4.json",
3
+ "data": {
4
+ "values": "<DVC_METRIC_DATA>"
5
+ },
6
+ "title": "<DVC_METRIC_TITLE>",
7
+ "mark": {
8
+ "type": "line"
9
+ },
10
+ "encoding": {
11
+ "x": {
12
+ "field": "<DVC_METRIC_X>",
13
+ "type": "quantitative",
14
+ "title": "<DVC_METRIC_X_LABEL>"
15
+ },
16
+ "y": {
17
+ "field": "<DVC_METRIC_Y>",
18
+ "type": "quantitative",
19
+ "title": "<DVC_METRIC_Y_LABEL>",
20
+ "scale": {
21
+ "zero": false
22
+ }
23
+ },
24
+ "color": {
25
+ "field": "rev",
26
+ "type": "nominal"
27
+ }
28
+ },
29
+ "transform": [
30
+ {
31
+ "loess": "<DVC_METRIC_Y>",
32
+ "on": "<DVC_METRIC_X>",
33
+ "groupby": [
34
+ "rev"
35
+ ],
36
+ "bandwidth": 0.3
37
+ }
38
+ ]
39
+ }
.dvcignore ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ # Add patterns of files dvc should ignore, which could improve
2
+ # the performance. Learn more at
3
+ # https://dvc.org/doc/user-guide/dvcignore
Makefile CHANGED
@@ -19,7 +19,7 @@ endif
19
  env:
20
  ifeq (True,$(HAS_CONDA))
21
  @echo ">>> Detected conda, creating conda environment."
22
- conda create --name $(PROJECT_NAME) python=3
23
  @echo ">>> New conda env created. Activate with:\nconda activate $(PROJECT_NAME)"
24
  else
25
  @echo ">>> No conda detected, creating venv environment."
 
19
  env:
20
  ifeq (True,$(HAS_CONDA))
21
  @echo ">>> Detected conda, creating conda environment."
22
+ conda create --name $(PROJECT_NAME) python=3.7.6
23
  @echo ">>> New conda env created. Activate with:\nconda activate $(PROJECT_NAME)"
24
  else
25
  @echo ">>> No conda detected, creating venv environment."
dvc.lock ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ process_data:
2
+ cmd: python3 src/code/make_dataset.py src/data/raw/nyu_depth_v2_labeled.mat src/data/raw/splits.mat
3
+ src/data/processed
4
+ deps:
5
+ - path: src/code/make_dataset.py
6
+ md5: b15a09b30657f303d62215ceb73c6f4f
7
+ - path: src/data/raw/nyu_depth_v2_labeled.mat
8
+ md5: 520609c519fba3ba5ac58c8fefcc3530
9
+ - path: src/data/raw/splits.mat
10
+ md5: 08e3c3aea27130ac7c01ffd739a4535f
11
+ outs:
12
+ - path: src/data/processed/
13
+ md5: 78d932b016048d527f9e73e1781127cd.dir
dvc.yaml ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ stages:
2
+ process_data:
3
+ cmd: python3 src/code/make_dataset.py src/data/raw/nyu_depth_v2_labeled.mat src/data/raw/splits.mat
4
+ src/data/processed
5
+ deps:
6
+ - src/code/make_dataset.py
7
+ - src/data/raw/nyu_depth_v2_labeled.mat
8
+ - src/data/raw/splits.mat
9
+ outs:
10
+ - src/data/processed/
requirements.txt CHANGED
@@ -1 +1,148 @@
1
- certifi==2020.6.20
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # This file may be used to create an environment using:
2
+ # $ conda create --name <env> --file <this file>
3
+ # platform: linux-64
4
+ _libgcc_mutex=0.1=main
5
+ appdirs=1.4.4=pypi_0
6
+ atpublic=2.0=pypi_0
7
+ attrs=19.3.0=py_0
8
+ backcall=0.2.0=pypi_0
9
+ beautifulsoup4=4.9.1=py37_0
10
+ blas=1.0=mkl
11
+ bottleneck=1.3.2=py37heb32a55_1
12
+ brotlipy=0.7.0=py37h7b6447c_1000
13
+ ca-certificates=2020.6.24=0
14
+ catalogue=1.0.0=py37_1
15
+ certifi=2020.6.20=py37_0
16
+ cffi=1.14.0=py37h2e261b9_0
17
+ chardet=3.0.4=py37_1003
18
+ colorama=0.4.3=pypi_0
19
+ commonmark=0.9.1=pypi_0
20
+ configobj=5.0.6=pypi_0
21
+ cryptography=2.9.2=py37h1ba5d50_0
22
+ cudatoolkit=10.2.89=hfd86e86_1
23
+ cycler=0.10.0=py37_0
24
+ cymem=2.0.3=py37he6710b0_0
25
+ cython-blis=0.4.1=py37h7b6447c_1
26
+ dataclasses=0.7=py37_0
27
+ decorator=4.4.2=pypi_0
28
+ dictdiffer=0.8.1=pypi_0
29
+ distro=1.5.0=pypi_0
30
+ dpath=2.0.1=pypi_0
31
+ dvc=1.6.0=pypi_0
32
+ fastai=1.0.61=1
33
+ fastcore=1.0.0=pyh39e3cac_0
34
+ fastprogress=1.0.0=pyh39e3cac_0
35
+ flatten-json=0.1.7=pypi_0
36
+ flufl-lock=3.2=pypi_0
37
+ freetype=2.10.2=h5ab3b9f_0
38
+ funcy=1.14=pypi_0
39
+ future=0.18.2=pypi_0
40
+ gitdb=4.0.5=pypi_0
41
+ gitpython=3.1.7=pypi_0
42
+ grandalf=0.6=pypi_0
43
+ h5py=2.10.0=pypi_0
44
+ idna=2.10=py_0
45
+ importlib-metadata=1.7.0=py37_0
46
+ importlib_metadata=1.7.0=0
47
+ intel-openmp=2020.1=217
48
+ ipython=7.17.0=pypi_0
49
+ ipython-genutils=0.2.0=pypi_0
50
+ jedi=0.17.2=pypi_0
51
+ joblib=0.16.0=py_0
52
+ jpeg=9b=h024ee3a_2
53
+ jsonpath-ng=1.5.1=pypi_0
54
+ jsonschema=3.0.2=py37_0
55
+ kiwisolver=1.2.0=py37hfd86e86_0
56
+ lcms2=2.11=h396b838_0
57
+ ld_impl_linux-64=2.33.1=h53a641e_7
58
+ libedit=3.1.20191231=h14c3975_1
59
+ libffi=3.2.1=hd88cf55_4
60
+ libgcc-ng=9.1.0=hdf63c60_0
61
+ libgfortran-ng=7.3.0=hdf63c60_0
62
+ libpng=1.6.37=hbc83047_0
63
+ libstdcxx-ng=9.1.0=hdf63c60_0
64
+ libtiff=4.1.0=h2733197_1
65
+ lz4-c=1.9.2=he6710b0_1
66
+ matplotlib=3.3.1=1
67
+ matplotlib-base=3.3.1=py37h817c723_0
68
+ mkl=2020.1=217
69
+ mkl-service=2.3.0=py37he904b0f_0
70
+ mkl_fft=1.1.0=py37h23d657b_0
71
+ mkl_random=1.1.1=py37h0573a6f_0
72
+ murmurhash=1.0.2=py37he6710b0_0
73
+ nanotime=0.5.2=pypi_0
74
+ ncurses=6.2=he6710b0_1
75
+ networkx=2.4=pypi_0
76
+ ninja=1.10.0=py37hfd86e86_0
77
+ numexpr=2.7.1=py37h423224d_0
78
+ numpy=1.19.1=py37hbc911f0_0
79
+ numpy-base=1.19.1=py37hfa32c7d_0
80
+ nvidia-ml-py3=7.352.0=py_0
81
+ olefile=0.46=py37_0
82
+ opencv-python=4.4.0.42=pypi_0
83
+ openssl=1.1.1g=h7b6447c_0
84
+ packaging=20.4=py_0
85
+ pandas=1.1.0=py37he6710b0_0
86
+ parso=0.7.1=pypi_0
87
+ pathspec=0.8.0=pypi_0
88
+ pexpect=4.8.0=pypi_0
89
+ pickleshare=0.7.5=pypi_0
90
+ pillow=7.2.0=py37hb39fc2d_0
91
+ pip=20.2.2=py37_0
92
+ plac=0.9.6=py37_1
93
+ ply=3.11=pypi_0
94
+ preshed=3.0.2=py37he6710b0_1
95
+ prompt-toolkit=3.0.6=pypi_0
96
+ ptyprocess=0.6.0=pypi_0
97
+ pyasn1=0.4.8=pypi_0
98
+ pycparser=2.20=py_2
99
+ pydot=1.4.1=pypi_0
100
+ pygments=2.6.1=pypi_0
101
+ pygtrie=2.3.2=pypi_0
102
+ pyopenssl=19.1.0=py_1
103
+ pyparsing=2.4.7=py_0
104
+ pyrsistent=0.16.0=py37h7b6447c_0
105
+ pysocks=1.7.1=py37_1
106
+ python=3.7.6=cpython_h8356626_6
107
+ python-dateutil=2.8.1=py_0
108
+ python_abi=3.7=1_cp37m
109
+ pytorch=1.6.0=py3.7_cuda10.2.89_cudnn7.6.5_0
110
+ pytz=2020.1=py_0
111
+ pyyaml=5.3.1=py37h7b6447c_1
112
+ readline=8.0=h7b6447c_0
113
+ requests=2.24.0=py_0
114
+ rich=5.2.1=pypi_0
115
+ ruamel-yaml=0.16.10=pypi_0
116
+ ruamel-yaml-clib=0.2.0=pypi_0
117
+ scikit-learn=0.23.1=py37h423224d_0
118
+ scipy=1.5.2=py37h0b6359f_0
119
+ setuptools=49.6.0=py37_0
120
+ shortuuid=1.0.1=pypi_0
121
+ shtab=1.3.1=pypi_0
122
+ six=1.15.0=py_0
123
+ smmap=3.0.4=pypi_0
124
+ soupsieve=2.0.1=py_0
125
+ spacy=2.3.1=py37hfd86e86_0
126
+ sqlite=3.33.0=h62c20be_0
127
+ srsly=1.0.2=py37he6710b0_0
128
+ tabulate=0.8.7=pypi_0
129
+ thinc=7.4.1=py37hfd86e86_0
130
+ threadpoolctl=2.1.0=pyh5ca1d4c_0
131
+ tk=8.6.10=hbc83047_0
132
+ toml=0.10.1=pypi_0
133
+ torchvision=0.7.0=py37_cu102
134
+ tornado=6.0.4=py37h7b6447c_1
135
+ tqdm=4.48.2=py_0
136
+ traitlets=4.3.3=pypi_0
137
+ typing-extensions=3.7.4.2=pypi_0
138
+ urllib3=1.25.10=py_0
139
+ voluptuous=0.11.7=pypi_0
140
+ wasabi=0.7.1=py_0
141
+ wcwidth=0.2.5=pypi_0
142
+ wheel=0.34.2=py37_0
143
+ xz=5.2.5=h7b6447c_0
144
+ yaml=0.2.5=h7b6447c_0
145
+ zc-lockfile=2.0=pypi_0
146
+ zipp=3.1.0=py_0
147
+ zlib=1.2.11=h7b6447c_3
148
+ zstd=1.4.5=h9ceee32_0
src/code/make_dataset.py CHANGED
@@ -1 +1,94 @@
1
- print('hello world')
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python
2
+ # -*- coding: utf-8 -*-
3
+ #######################################################################################
4
+ # The MIT License
5
+
6
+ # Copyright (c) 2014 Hannes Schulz, University of Bonn <schulz@ais.uni-bonn.de>
7
+ # Copyright (c) 2013 Benedikt Waldvogel, University of Bonn <mail@bwaldvogel.de>
8
+ # Copyright (c) 2008-2009 Sebastian Nowozin <nowozin@gmail.com>
9
+
10
+ # Permission is hereby granted, free of charge, to any person obtaining a copy
11
+ # of this software and associated documentation files (the "Software"), to deal
12
+ # in the Software without restriction, including without limitation the rights
13
+ # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
14
+ # copies of the Software, and to permit persons to whom the Software is
15
+ # furnished to do so, subject to the following conditions:
16
+ #
17
+ # The above copyright notice and this permission notice shall be included in all
18
+ # copies or substantial portions of the Software.
19
+ #
20
+ # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
21
+ # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
22
+ # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
23
+ # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
24
+ # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
25
+ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
26
+ # SOFTWARE.
27
+ #######################################################################################
28
+ #
29
+ # Helper script to convert the NYU Depth v2 dataset Matlab file into a set of
30
+ # PNG and JPEG images.
31
+ #
32
+ # See https://github.com/deeplearningais/curfil/wiki/Training-and-Prediction-with-the-NYU-Depth-v2-Dataset
33
+
34
+ from __future__ import print_function
35
+
36
+ import h5py
37
+ import numpy as np
38
+ import os
39
+ import scipy.io
40
+ import sys
41
+ import cv2
42
+
43
+
44
+ def convert_image(i, scene, depth, image, folder):
45
+ img_depth = depth * 1000.0
46
+ img_depth_uint16 = img_depth.astype(np.uint16)
47
+ cv2.imwrite("%s/%05d_depth.png" % (folder, i), img_depth_uint16)
48
+
49
+ image = image[:, :, ::-1]
50
+ image_black_boundary = np.zeros((480, 640, 3), dtype=np.uint8)
51
+ image_black_boundary[7:474, 7:632, :] = image[7:474, 7:632, :]
52
+ cv2.imwrite("%s/%05d.jpg" % (folder, i), image_black_boundary)
53
+
54
+
55
+ if __name__ == "__main__":
56
+
57
+ if len(sys.argv) < 4:
58
+ print("usage: %s <h5_file> <train_test_split> <out_folder>" % sys.argv[0], file=sys.stderr)
59
+ sys.exit(0)
60
+
61
+ h5_file = h5py.File(sys.argv[1], "r")
62
+ # h5py is not able to open that file. but scipy is
63
+ train_test = scipy.io.loadmat(sys.argv[2])
64
+ out_folder = sys.argv[3]
65
+
66
+ test_images = set([int(x) for x in train_test["testNdxs"]])
67
+ train_images = set([int(x) for x in train_test["trainNdxs"]])
68
+ print("%d training images" % len(train_images))
69
+ print("%d test images" % len(test_images))
70
+
71
+ depth = h5_file['depths']
72
+
73
+ print("reading", sys.argv[1])
74
+
75
+ images = h5_file['images']
76
+ scenes = [u''.join(chr(c) for c in h5_file[obj_ref]) for obj_ref in h5_file['sceneTypes'][0]]
77
+
78
+ print("processing images")
79
+ for i, image in enumerate(images):
80
+ print("image", i + 1, "/", len(images))
81
+
82
+ idx = int(i) + 1
83
+ if idx in train_images:
84
+ train_test = "train"
85
+ else:
86
+ assert idx in test_images, "index %d neither found in training set nor in test set" % idx
87
+ train_test = "test"
88
+
89
+ folder = "%s/%s/%s" % (out_folder, train_test, scenes[i])
90
+ if not os.path.exists(folder):
91
+ os.makedirs(folder)
92
+ convert_image(i, scenes[i], depth[i, :, :].T, image.T, folder)
93
+
94
+ print("Finished")
src/code/training.py ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import sys
3
+ from fastai.vision import unet_learner, ImageImageList, models, Path, root_mean_squared_error
4
+
5
+
6
+ def get_y_fn(x):
7
+ y = str(x.absolute()).replace('.jpg', '_depth.png')
8
+ y = Path(y)
9
+
10
+ return y
11
+
12
+
13
+ def create_databunch(data_path):
14
+ data = (ImageImageList.from_folder(data_path)
15
+ .filter_by_func(lambda fname: fname.suffix == '.jpg')
16
+ .split_by_folder(train='train', valid='test')
17
+ .label_from_func(get_y_fn).databunch()).normalize()
18
+ return data
19
+
20
+
21
+ def train(data):
22
+ learner = unet_learner(data, models.resnet34, metrics=root_mean_squared_error, wd=1e-2, loss_func=torch.nn.SmoothL1Loss())
23
+ learner.fit_one_cycle(1, 1e-3)
24
+
25
+
26
+ if __name__ == "__main__":
27
+ if len(sys.argv) < 3:
28
+ print("usage: %s <data_path> <out_folder>" % sys.argv[0], file=sys.stderr)
29
+ sys.exit(0)
30
+
31
+ data = create_databunch(sys.argv[1])
32
+ data.batch_size = 1
33
+ data.num_workers = 0
34
+ learner = train(data)
35
+
36
+ learner.save(sys.argv[2])
37
+ learner.show_results()
src/data/.gitignore ADDED
@@ -0,0 +1 @@
 
 
1
+ /processed
src/data/raw/.gitignore ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ /nyu_depth_v2_labeled.mat
2
+ /splits.mat
src/data/raw/nyu_depth_v2_labeled.mat.dvc ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ md5: d27a0ba6c898f981797a3388c26c2d0f
2
+ frozen: true
3
+ deps:
4
+ - etag: '"b125b2b1-5aa5b95864fc7"'
5
+ path: http://horatio.cs.nyu.edu/mit/silberman/nyu_depth_v2/nyu_depth_v2_labeled.mat
6
+ outs:
7
+ - md5: 520609c519fba3ba5ac58c8fefcc3530
8
+ path: nyu_depth_v2_labeled.mat
src/data/raw/splits.mat.dvc ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ md5: 26011289311c18b92781de66654223a4
2
+ frozen: true
3
+ deps:
4
+ - etag: '"a42-4cb6a5fad2fc0"'
5
+ path: http://horatio.cs.nyu.edu/mit/silberman/indoor_seg_sup/splits.mat
6
+ outs:
7
+ - md5: 08e3c3aea27130ac7c01ffd739a4535f
8
+ path: splits.mat