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Pycocotools best.pt after COCO train (#1593)

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Glenn Jocher 2020-12-06 14:58:50 +01:00 committed by GitHub
parent adc49abc71
commit 4a07280884
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3 changed files with 25 additions and 15 deletions
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5
test.py
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@ -1,5 +1,4 @@
import argparse import argparse
import glob
import json import json
import os import os
from pathlib import Path from pathlib import Path
@ -246,7 +245,7 @@ def test(data,
# Save JSON # Save JSON
if save_json and len(jdict): if save_json and len(jdict):
w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
anno_json = glob.glob('../coco/annotations/instances_val*.json')[0] # annotations json anno_json = '../coco/annotations/instances_val2017.json' # annotations json
pred_json = str(save_dir / f"{w}_predictions.json") # predictions json pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
print('\nEvaluating pycocotools mAP... saving %s...' % pred_json) print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
with open(pred_json, 'w') as f: with open(pred_json, 'w') as f:
@ -266,7 +265,7 @@ def test(data,
eval.summarize() eval.summarize()
map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5)
except Exception as e: except Exception as e:
print('ERROR: pycocotools unable to run: %s' % e) print(f'pycocotools unable to run: {e}')
# Return results # Return results
if not training: if not training:

33
train.py
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@ -22,6 +22,7 @@ from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm from tqdm import tqdm
import test # import test.py to get mAP after each epoch import test # import test.py to get mAP after each epoch
from models.experimental import attempt_load
from models.yolo import Model from models.yolo import Model
from utils.autoanchor import check_anchors from utils.autoanchor import check_anchors
from utils.datasets import create_dataloader from utils.datasets import create_dataloader
@ -193,9 +194,9 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
# Process 0 # Process 0
if rank in [-1, 0]: if rank in [-1, 0]:
ema.updates = start_epoch * nb // accumulate # set EMA updates ema.updates = start_epoch * nb // accumulate # set EMA updates
testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, # testloader
hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True,
rank=-1, world_size=opt.world_size, workers=opt.workers)[0] # testloader rank=-1, world_size=opt.world_size, workers=opt.workers, pad=0.5)[0]
if not opt.resume: if not opt.resume:
labels = np.concatenate(dataset.labels, 0) labels = np.concatenate(dataset.labels, 0)
@ -385,15 +386,12 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
if rank in [-1, 0]: if rank in [-1, 0]:
# Strip optimizers # Strip optimizers
n = opt.name if opt.name.isnumeric() else '' for f in [last, best]:
fresults, flast, fbest = save_dir / f'results{n}.txt', wdir / f'last{n}.pt', wdir / f'best{n}.pt' if f.exists(): # is *.pt
for f1, f2 in zip([wdir / 'last.pt', wdir / 'best.pt', results_file], [flast, fbest, fresults]): strip_optimizer(f) # strip optimizer
if f1.exists(): os.system('gsutil cp %s gs://%s/weights' % (f, opt.bucket)) if opt.bucket else None # upload
os.rename(f1, f2) # rename
if str(f2).endswith('.pt'): # is *.pt # Plots
strip_optimizer(f2) # strip optimizer
os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket else None # upload
# Finish
if plots: if plots:
plot_results(save_dir=save_dir) # save as results.png plot_results(save_dir=save_dir) # save as results.png
if wandb: if wandb:
@ -401,6 +399,19 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files
if (save_dir / f).exists()]}) if (save_dir / f).exists()]})
logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
# Test best.pt
if opt.data.endswith('coco.yaml') and nc == 80: # if COCO
results, _, _ = test.test(opt.data,
batch_size=total_batch_size,
imgsz=imgsz_test,
model=attempt_load(best if best.exists() else last, device).half(),
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
save_json=True, # use pycocotools
plots=False)
else: else:
dist.destroy_process_group() dist.destroy_process_group()

2
utils/google_utils.py
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@ -17,7 +17,7 @@ def gsutil_getsize(url=''):
def attempt_download(weights): def attempt_download(weights):
# Attempt to download pretrained weights if not found locally # Attempt to download pretrained weights if not found locally
weights = weights.strip().replace("'", '') weights = str(weights).strip().replace("'", '')
file = Path(weights).name.lower() file = Path(weights).name.lower()
msg = weights + ' missing, try downloading from https://github.com/ultralytics/yolov3/releases/' msg = weights + ' missing, try downloading from https://github.com/ultralytics/yolov3/releases/'