-
Notifications
You must be signed in to change notification settings - Fork 1.4k
/
benchmarks.py
142 lines (125 loc) · 6.12 KB
/
benchmarks.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
import argparse
import platform
import sys
import time
from pathlib import Path
import pandas as pd
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLO root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
# ROOT = ROOT.relative_to(Path.cwd()) # relative
import export
from models.experimental import attempt_load
from models.yolo import SegmentationModel
from segment.val import run as val_seg
from utils import notebook_init
from utils.general import LOGGER, check_yaml, file_size, print_args
from utils.torch_utils import select_device
from val import run as val_det
def run(
weights=ROOT / 'yolo.pt', # weights path
imgsz=640, # inference size (pixels)
batch_size=1, # batch size
data=ROOT / 'data/coco.yaml', # dataset.yaml path
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
half=False, # use FP16 half-precision inference
test=False, # test exports only
pt_only=False, # test PyTorch only
hard_fail=False, # throw error on benchmark failure
):
y, t = [], time.time()
device = select_device(device)
model_type = type(attempt_load(weights, fuse=False)) # DetectionModel, SegmentationModel, etc.
for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, CPU, GPU)
try:
assert i not in (9, 10), 'inference not supported' # Edge TPU and TF.js are unsupported
assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13' # CoreML
if 'cpu' in device.type:
assert cpu, 'inference not supported on CPU'
if 'cuda' in device.type:
assert gpu, 'inference not supported on GPU'
# Export
if f == '-':
w = weights # PyTorch format
else:
w = export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # all others
assert suffix in str(w), 'export failed'
# Validate
if model_type == SegmentationModel:
result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half)
metric = result[0][7] # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls))
else: # DetectionModel:
result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half)
metric = result[0][3] # (p, r, map50, map, *loss(box, obj, cls))
speed = result[2][1] # times (preprocess, inference, postprocess)
y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)]) # MB, mAP, t_inference
except Exception as e:
if hard_fail:
assert type(e) is AssertionError, f'Benchmark --hard-fail for {name}: {e}'
LOGGER.warning(f'WARNING ⚠️ Benchmark failure for {name}: {e}')
y.append([name, None, None, None]) # mAP, t_inference
if pt_only and i == 0:
break # break after PyTorch
# Print results
LOGGER.info('\n')
parse_opt()
notebook_init() # print system info
c = ['Format', 'Size (MB)', 'mAP50-95', 'Inference time (ms)'] if map else ['Format', 'Export', '', '']
py = pd.DataFrame(y, columns=c)
LOGGER.info(f'\nBenchmarks complete ({time.time() - t:.2f}s)')
LOGGER.info(str(py if map else py.iloc[:, :2]))
if hard_fail and isinstance(hard_fail, str):
metrics = py['mAP50-95'].array # values to compare to floor
floor = eval(hard_fail) # minimum metric floor to pass
assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: mAP50-95 < floor {floor}'
return py
def test(
weights=ROOT / 'yolo.pt', # weights path
imgsz=640, # inference size (pixels)
batch_size=1, # batch size
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
half=False, # use FP16 half-precision inference
test=False, # test exports only
pt_only=False, # test PyTorch only
hard_fail=False, # throw error on benchmark failure
):
y, t = [], time.time()
device = select_device(device)
for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable)
try:
w = weights if f == '-' else \
export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # weights
assert suffix in str(w), 'export failed'
y.append([name, True])
except Exception:
y.append([name, False]) # mAP, t_inference
# Print results
LOGGER.info('\n')
parse_opt()
notebook_init() # print system info
py = pd.DataFrame(y, columns=['Format', 'Export'])
LOGGER.info(f'\nExports complete ({time.time() - t:.2f}s)')
LOGGER.info(str(py))
return py
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default=ROOT / 'yolo.pt', help='weights path')
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
parser.add_argument('--test', action='store_true', help='test exports only')
parser.add_argument('--pt-only', action='store_true', help='test PyTorch only')
parser.add_argument('--hard-fail', nargs='?', const=True, default=False, help='Exception on error or < min metric')
opt = parser.parse_args()
opt.data = check_yaml(opt.data) # check YAML
print_args(vars(opt))
return opt
def main(opt):
test(**vars(opt)) if opt.test else run(**vars(opt))
if __name__ == "__main__":
opt = parse_opt()
main(opt)