forked from DeepLabCut/DeepLabCut
-
Notifications
You must be signed in to change notification settings - Fork 0
/
testscript_deterministicwithResNet152.py
144 lines (117 loc) · 4.32 KB
/
testscript_deterministicwithResNet152.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# DeepLabCut Toolbox (deeplabcut.org)
# © A. & M.W. Mathis Labs
# https://github.com/DeepLabCut/DeepLabCut
#
# Please see AUTHORS for contributors.
# https://github.com/DeepLabCut/DeepLabCut/blob/master/AUTHORS
#
# Licensed under GNU Lesser General Public License v3.0
#
"""
Created on Tue Oct 2 13:56:11 2018
@author: alex
DEVELOPERS:
This script tests various functionalities in an automatic way.
Note that the same ResNet 152 is trained 4 times (twice with the standard loader).
The sequence of losses is different....
Then twice with the 'deterministic' loader. The losses are identical when this is done twice.
I.e. I get twice: ;)
iteration: 1 loss: 1.6505 lr: 0.001
iteration: 2 loss: 0.6929 lr: 0.001
iteration: 3 loss: 0.6420 lr: 0.001
iteration: 4 loss: 0.5579 lr: 0.001
iteration: 5 loss: 0.4746 lr: 0.001
iteration: 6 loss: 0.3366 lr: 0.001
iteration: 7 loss: 0.3194 lr: 0.001
iteration: 8 loss: 0.2561 lr: 0.001
iteration: 9 loss: 0.1964 lr: 0.001
iteration: 10 loss: 0.1220 lr: 0.001
It produces nothing of interest scientifically.
"""
task = "TEST-deterministic" # Enter the name of your experiment Task
scorer = "Alex" # Enter the name of the experimenter/labeler
import os, subprocess, deeplabcut
from pathlib import Path
import pandas as pd
import numpy as np
print("Imported DLC!")
basepath = os.path.dirname(os.path.abspath("testscript.py"))
videoname = "reachingvideo1"
video = [
os.path.join(
basepath, "Reaching-Mackenzie-2018-08-30", "videos", videoname + ".avi"
)
]
# to test destination folder:
# dfolder=basepath
dfolder = None
print("CREATING PROJECT")
path_config_file = deeplabcut.create_new_project(task, scorer, video, copy_videos=True)
cfg = deeplabcut.auxiliaryfunctions.read_config(path_config_file)
cfg["numframes2pick"] = 5
cfg["pcutoff"] = 0.01
cfg["TrainingFraction"] = [0.8]
cfg["default_net_type"] = "resnet_152" #'mobilenet_v2_0.35'
deeplabcut.auxiliaryfunctions.write_config(path_config_file, cfg)
print("EXTRACTING FRAMES")
deeplabcut.extract_frames(path_config_file, mode="automatic", userfeedback=False)
print("CREATING-SOME LABELS FOR THE FRAMES")
frames = os.listdir(os.path.join(cfg["project_path"], "labeled-data", videoname))
# As this next step is manual, we update the labels by putting them on the diagonal (fixed for all frames)
for index, bodypart in enumerate(cfg["bodyparts"]):
columnindex = pd.MultiIndex.from_product(
[[scorer], [bodypart], ["x", "y"]], names=["scorer", "bodyparts", "coords"]
)
frame = pd.DataFrame(
100 + np.ones((len(frames), 2)) * 50 * index,
columns=columnindex,
index=[os.path.join("labeled-data", videoname, fn) for fn in frames],
)
if index == 0:
dataFrame = frame
else:
dataFrame = pd.concat([dataFrame, frame], axis=1)
dataFrame.to_csv(
os.path.join(
cfg["project_path"],
"labeled-data",
videoname,
"CollectedData_" + scorer + ".csv",
)
)
dataFrame.to_hdf(
os.path.join(
cfg["project_path"],
"labeled-data",
videoname,
"CollectedData_" + scorer + ".h5",
),
"df_with_missing",
format="table",
mode="w",
)
print("Plot labels...")
deeplabcut.check_labels(path_config_file)
print("CREATING TRAININGSET")
deeplabcut.create_training_dataset(path_config_file)
# posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),'train/pose_cfg.yaml')
shuffle = 1
posefile, _, _ = deeplabcut.return_train_network_path(path_config_file, shuffle=shuffle)
print("CHANGING training parameters to end quickly!")
edits = {"save_iters": 4, "display_iters": 1, "multi_step": [[0.001, 10]]}
DLC_config = deeplabcut.auxiliaryfunctions.edit_config(posefile, edits)
print("TRAIN")
deeplabcut.train_network(path_config_file)
print("TRAIN again... different loss?")
deeplabcut.train_network(path_config_file)
DLC_config = deeplabcut.auxiliaryfunctions.edit_config(
posefile, {"dataset_type": "deterministic", "deterministic": True}
)
print("TRAIN")
deeplabcut.train_network(path_config_file)
print("TRAIN again... the same losses!")
deeplabcut.train_network(path_config_file)
print("ALL DONE!!! - deterministic at least runs... were the losses identical?")