-
Notifications
You must be signed in to change notification settings - Fork 0
/
batch_experiments.py
251 lines (205 loc) · 12.8 KB
/
batch_experiments.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
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
#import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME)
import random
import os
import json
import argparse
import shutil
import re
import openpyxl
import glob
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('id', type=int, action="store")
args = parser.parse_args()
import os
os.environ["CUDA_VISIBLE_DEVICES"]=f"{args.id}"
from detectron2.engine import launch
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
# import some common libraries
import matplotlib.pyplot as plt
import numpy as np
import cv2 as cv
import importlib.util
from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer
from detectron2.engine import DefaultTrainer
from detectron2.config import get_cfg
# import some common detectron2 utilities
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog, DatasetCatalog
from detectron2.data.datasets import load_coco_json, register_coco_instances
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
from detectron2.evaluation.coco_evaluation import _evaluate_predictions_on_coco
from detectron2.data import build_detection_test_loader
from detectron2.modeling import build_model
spec = importlib.util.spec_from_file_location("custom_datasets", os.path.join(os.path.dirname(__file__), 'custom_datasets.py'))
custom_datasets = importlib.util.module_from_spec(spec)
spec.loader.exec_module(custom_datasets)
DATASETS_REAL = ['guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7']
DATASETS_SYTH = ['guns_edgecase', 'unity_synthetic_500', 'unity_synthetic_1000', 'unity_synthetic_2500', 'unity_synthetic_5000']
SHOW_DATASET = False
TRAIN = False
SAVE_DATASET_PREDICTIONS = False
SHOW_PREVIEW = False
PREVIEW_ON_DIR = False
MAKE_ANNOS = False
SHOW_EVALUATION = True
class JsonCOCOEvaluator(COCOEvaluator):
def __init__(self, *arg, **kwargs):
super().__init__(*arg, **kwargs)
with open(os.path.join(self._output_dir, 'coco_instances_results.json')) as f:
self._coco_results = json.load(f)
def evaluate(self, theshold):
task ='bbox'
coco_eval = (
_evaluate_predictions_on_coco(
self._coco_api, self._coco_results, task, kpt_oks_sigmas=self._kpt_oks_sigmas
)
if len(self._coco_results) > 0
else None # cocoapi does not handle empty results very well
)
res = self._derive_coco_results(
coco_eval, task, class_names=self._metadata.get("thing_classes")
)
FP = FN = TP = 0.
prob_preds = []
for cocoRes in coco_eval.evalImgs[:int(len(coco_eval.evalImgs)/4)]:
if cocoRes is None:
continue
for det, score in zip(cocoRes['dtMatches'][0], cocoRes['dtScores']):
# No gt match
if det == 0.:
prob_preds.append([0, score])
if score >= theshold:
FP += 1
else:
prob_preds.append([1, score])
if score >= theshold:
TP += 1
else:
FN += 1
for gt in cocoRes['gtMatches'][0]:
if gt == 0.:
prob_preds.append([1, 0.])
FN += 1
res.update({'TP': TP, 'FP': FP, 'FN': FN, 'ProbPreds': prob_preds})
return {task: res}
custom_datasets.loadDatasets()
EXPERIMENTS_OUTPUT_PATH = '/mnt/datos/experiments/guns_detection'
networks = {
'faster_rcnn_R_50_FPN_1x': {
'cfg': '../../build/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml',
'weights': 'detectron2://COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl'
},
'faster101': {
'cfg': '../../build/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml',
'weights': 'detectron2://COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x/139173657/model_final_68b088.pkl'
},
}
experiments = [
{'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_edgecase',), 'test': ("guns_granada_test",)},
{'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_500',), 'test': ("guns_granada_test",),},
{'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_1000',), 'test': ("guns_granada_test",),},
{'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_2500',), 'test': ("guns_granada_test",),},
{'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('guns_edgecase',)]/model_0039999.pth"},
{'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('unity_synthetic_500',)]/model_0039999.pth"},
{'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('unity_synthetic_1000',)]/model_0039999.pth"},
{'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('unity_synthetic_2500',)]/model_0039999.pth"},
{'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7',), 'test': ("guns_granada_test",), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('guns_edgecase',)]/model_0039999.pth"},
{'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7',), 'test': ("guns_granada_test",), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('unity_synthetic_500',)]/model_0039999.pth"},
{'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7',), 'test': ("guns_granada_test",), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('unity_synthetic_2500',)]/model_0039999.pth"},
{'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train',), 'test': ("guns_granada_test",)},
{'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7'), 'test': ("guns_granada_test",)},
{'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_simulacro_1', 'guns_simulacro_7'), 'test': ("guns_granada_test",)},
{'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train',), 'test': ("guns_granada_test",)},
{'steps': 40000, 'lr': 0.002, 'net': 'faster101', 'train': ('guns_edgecase',), 'test': ('guns_granada_test',),},
{'steps': 40000, 'lr': 0.002, 'net': 'faster101', 'train': ('unity_synthetic_500',), 'test': ('guns_granada_test',),},
{'steps': 40000, 'lr': 0.002, 'net': 'faster101', 'train': ('unity_synthetic_1000',), 'test': ('guns_granada_test',),},
{'steps': 40000, 'lr': 0.002, 'net': 'faster101', 'train': ('unity_synthetic_2500',), 'test': ('guns_granada_test',),},
{'steps': 80000, 'lr': 0.002, 'net': 'faster101', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster101[LR=0.002][('guns_edgecase',)]/model_0039999.pth"},
{'steps': 80000, 'lr': 0.002, 'net': 'faster101', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster101[LR=0.002][('unity_synthetic_500',)]/model_0039999.pth"},
{'steps': 80000, 'lr': 0.002, 'net': 'faster101', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster101[LR=0.002][('unity_synthetic_1000',)]/model_0039999.pth"},
{'steps': 80000, 'lr': 0.002, 'net': 'faster101', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster101[LR=0.002][('unity_synthetic_2500',)]/model_0039999.pth"},
{'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_edgecase',),},
{'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_edgecase', 'guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7'), 'test': ("guns_granada_test",),},
{'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_edgecase', 'guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7', 'alamy'), 'test': ("guns_granada_test",), 'test': ("guns_granada_test",),},
{'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_1000',), 'test': ("guns_granada_test",),},
{'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_2500',), 'test': ("guns_granada_test",),},
{'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_1000', 'guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7'), 'test': ("guns_granada_test",),},
{'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_2500', 'guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7'), 'test': ("guns_granada_test",),},
]
def train(exp):
print(f'Executing experiment: \n{exp}\n')
cfg = get_cfg()
cfg.merge_from_file(networks[exp['net']]['cfg'])
cfg.MODEL.WEIGHTS = exp.get('from', networks[exp['net']]['weights'])
cfg.INPUT.MAX_SIZE_TEST = 2500
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
cfg.DATALOADER.NUM_WORKERS = 2
cfg.DATASETS.TRAIN = exp['train']
cfg.DATASETS.TEST = exp['test']
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.SOLVER.BASE_LR = exp['lr']
cfg.SOLVER.MAX_ITER = exp['steps'] # 300 iterations seems good enough, but you can certainly train longer
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True
cfg.SOLVER.CHECKPOINT_PERIOD = 5000
cfg.TEST.EVAL_PERIOD = 0
#cfg.INPUT.MIN_SIZE_TRAIN = (480, 512, 640, 672, 704, 736, 768, 800)
if 'from' in exp:
trainFromStr = exp['from'].split('/')[-2]
cfg.OUTPUT_DIR = f"{EXPERIMENTS_OUTPUT_PATH}/{exp['net']}[LR={exp['lr']}][{str(exp['train'])}]FROM[{trainFromStr}]"
#if not os.path.exists(cfg.OUTPUT_DIR):
# print(f"copying {trainFromFolder}\nto {cfg.OUTPUT_DIR}")
# shutil.copytree(trainFromFolder, cfg.OUTPUT_DIR)
else:
cfg.OUTPUT_DIR = f"{EXPERIMENTS_OUTPUT_PATH}/{exp['net']}[LR={exp['lr']}][{str(exp['train'])}]"
cfg.freeze()
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
class Trainer(DefaultTrainer):
@classmethod
def build_evaluator(cls, cfg_, dataset_name):
return COCOEvaluator(dataset_name, cfg_, distributed=False)
trainer = Trainer(cfg)
trainer.resume_or_load(resume=True)
trainer.train()
def test(exp, selected_dataset, theshold):
print(f'Testing experiment: \n{exp}\n')
cfg = get_cfg()
cfg.merge_from_file(networks[exp['net']]['cfg'])
cfg.DATASETS.TEST = exp['test']
cfg.INPUT.MAX_SIZE_TEST = 2500
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
cfg.DATALOADER.NUM_WORKERS = 2
if 'from' in exp:
trainFromStr = exp['from'].split('/')[-2]
cfg.OUTPUT_DIR = f"{EXPERIMENTS_OUTPUT_PATH}/{exp['net']}[LR={exp['lr']}][{str(exp['train'])}]FROM[{trainFromStr}]"
else:
cfg.OUTPUT_DIR = f"{EXPERIMENTS_OUTPUT_PATH}/{exp['net']}[LR={exp['lr']}][{str(exp['train'])}]"
cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, 'model_final.pth')
if all([ds in DATASETS_REAL or ds in DATASETS_SYTH for ds in exp['train']]):
output_dir = f"{cfg.OUTPUT_DIR}/{selected_dataset}"
model = build_model(cfg)
DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=False
)
evaluator = COCOEvaluator(selected_dataset, cfg, False, output_dir=output_dir)
val_loader = build_detection_test_loader(cfg, selected_dataset)
inference_on_dataset(model, val_loader, evaluator)
if __name__ == '__main__':
module = args.id - 1
size = 2
num_gpus = 2
port = 2 ** 15 + 2 ** 14 + hash(os.getuid()) % 2 ** 14
for i, exp in enumerate(experiments):
if i % size != module:
continue
train(exp)
# launch(
# train,
# num_gpus,
# dist_url="tcp://127.0.0.1:{}".format(port),
# args=(exp,)
# )