Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add File Size (MB) column to benchmarks #8359

Merged
merged 3 commits into from
Jun 27, 2022
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 5 additions & 4 deletions utils/benchmarks.py
Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@
import export
import val
from utils import notebook_init
from utils.general import LOGGER, check_yaml, print_args
from utils.general import LOGGER, check_yaml, file_size, print_args
from utils.torch_utils import select_device


Expand Down Expand Up @@ -75,18 +75,19 @@ def run(
result = val.run(data, w, batch_size, imgsz, plots=False, device=device, task='benchmark', half=half)
metrics = result[0] # metrics (mp, mr, map50, map, *losses(box, obj, cls))
speeds = result[2] # times (preprocess, inference, postprocess)
y.append([name, round(metrics[3], 4), round(speeds[1], 2)]) # mAP, t_inference
y.append([name, round(file_size(w), 1), round(metrics[3], 4), round(speeds[1], 2)]) # MB, mAP, t_inference
except Exception as e:
LOGGER.warning(f'WARNING: Benchmark failure for {name}: {e}')
y.append([name, None, None]) # mAP, t_inference
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
py = pd.DataFrame(y, columns=['Format', 'mAP@0.5:0.95', 'Inference time (ms)'] if map else ['Format', 'Export', ''])
c = ['Format', 'Size (MB)', 'mAP@0.5:0.95', '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]))
return py
Expand Down