updates
This commit is contained in:
Glenn Jocher
parent
96e25462e8
commit
8501aed49f
|
|
@ -50,7 +50,7 @@ https://colab.research.google.com/drive/1G8T-VFxQkjDe4idzN8F-hbIBqkkkQnxw
|
|||
|
||||
**Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`.
|
||||
|
||||
**Resume Training:** `python3 train.py --resume` to resume training from `weights/latest.pt`.
|
||||
**Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt`.
|
||||
|
||||
Each epoch trains on 117,263 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. Default training settings produce loss plots below, with **training speed of 0.25 s/batch on a V100 GPU (almost 50 COCO epochs/day)**.
|
||||
|
||||
|
|
@ -136,8 +136,9 @@ Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
|
|||
|
||||
# mAP
|
||||
|
||||
- Use `test.py --weights weights/yolov3.weights` to test the official YOLOv3 weights.
|
||||
- Use `test.py --weights weights/latest.pt` to test the latest training results.
|
||||
- `test.py --weights weights/yolov3.weights` to test official YOLOv3 weights.
|
||||
- `test.py --weights weights/last.pt` to test most recent checkpoint.
|
||||
- `test.py --weights weights/best.pt` to test best checkpoint.
|
||||
- Compare to darknet published results https://arxiv.org/abs/1804.02767.
|
||||
|
||||
<!---
|
||||
|
|
|
|||
14
train.py
14
train.py
|
|
@ -45,7 +45,7 @@ def train(cfg,
|
|||
# Initialize
|
||||
init_seeds()
|
||||
weights = 'weights' + os.sep
|
||||
latest = weights + 'latest.pt'
|
||||
last = weights + 'last.pt'
|
||||
best = weights + 'best.pt'
|
||||
device = torch_utils.select_device()
|
||||
multi_scale = opt.multi_scale
|
||||
|
|
@ -79,10 +79,10 @@ def train(cfg,
|
|||
for p in model.parameters():
|
||||
p.requires_grad = True if p.shape[0] == nf else False
|
||||
|
||||
else: # resume from latest.pt
|
||||
else: # resume from last.pt
|
||||
if opt.bucket:
|
||||
os.system('gsutil cp gs://%s/latest.pt %s' % (opt.bucket, latest)) # download from bucket
|
||||
chkpt = torch.load(latest, map_location=device) # load checkpoint
|
||||
os.system('gsutil cp gs://%s/last.pt %s' % (opt.bucket, last)) # download from bucket
|
||||
chkpt = torch.load(last, map_location=device) # load checkpoint
|
||||
model.load_state_dict(chkpt['model'])
|
||||
|
||||
if chkpt['optimizer'] is not None:
|
||||
|
|
@ -273,10 +273,10 @@ def train(cfg,
|
|||
model) is nn.parallel.DistributedDataParallel else model.state_dict(),
|
||||
'optimizer': optimizer.state_dict()}
|
||||
|
||||
# Save latest checkpoint
|
||||
torch.save(chkpt, latest)
|
||||
# Save last checkpoint
|
||||
torch.save(chkpt, last)
|
||||
if opt.bucket:
|
||||
os.system('gsutil cp %s gs://%s' % (latest, opt.bucket)) # upload to bucket
|
||||
os.system('gsutil cp %s gs://%s' % (last, opt.bucket)) # upload to bucket
|
||||
|
||||
# Save best checkpoint
|
||||
if best_fitness == fitness:
|
||||
|
|
|
|||
|
|
@ -181,9 +181,13 @@ class LoadImagesAndLabels(Dataset): # for training/testing
|
|||
s = [exif_size(Image.open(f)) for f in tqdm(self.img_files, desc='Reading image shapes')]
|
||||
np.savetxt(sp, s, fmt='%g')
|
||||
|
||||
with open(sp, 'r') as f: # read existing shapefile
|
||||
s = np.array([x.split() for x in f.read().splitlines()], dtype=np.float64)
|
||||
assert len(s) == n, 'Shapefile error. Please delete %s and rerun' % sp # TODO: auto-delete shapefile
|
||||
try:
|
||||
with open(sp, 'r') as f: # read existing shapefile
|
||||
s = np.array([x.split() for x in f.read().splitlines()], dtype=np.float64)
|
||||
assert len(s) == n, 'Shapefile out of sync'
|
||||
except:
|
||||
os.remove(sp)
|
||||
print('Shapefile deleted: %s. Please rerun again.' % sp)
|
||||
|
||||
# Sort by aspect ratio
|
||||
ar = s[:, 1] / s[:, 0] # aspect ratio
|
||||
|
|
|
|||
12
utils/gcp.sh
12
utils/gcp.sh
|
|
@ -35,12 +35,12 @@ git pull https://github.com/ultralytics/yolov3 test # branch
|
|||
# Test Darknet training
|
||||
python3 test.py --weights ../darknet/backup/yolov3.backup
|
||||
|
||||
# Copy latest.pt TO bucket
|
||||
gsutil cp yolov3/weights/latest1gpu.pt gs://ultralytics
|
||||
# Copy last.pt TO bucket
|
||||
gsutil cp yolov3/weights/last1gpu.pt gs://ultralytics
|
||||
|
||||
# Copy latest.pt FROM bucket
|
||||
gsutil cp gs://ultralytics/latest.pt yolov3/weights/latest.pt
|
||||
wget https://storage.googleapis.com/ultralytics/yolov3/latest_v1_0.pt -O weights/latest_v1_0.pt
|
||||
# Copy last.pt FROM bucket
|
||||
gsutil cp gs://ultralytics/last.pt yolov3/weights/last.pt
|
||||
wget https://storage.googleapis.com/ultralytics/yolov3/last_v1_0.pt -O weights/last_v1_0.pt
|
||||
wget https://storage.googleapis.com/ultralytics/yolov3/best_v1_0.pt -O weights/best_v1_0.pt
|
||||
|
||||
# Reproduce tutorials
|
||||
|
|
@ -94,7 +94,7 @@ python3 test.py --data ../supermarket2/supermarket2.data --weights ../darknet/ba
|
|||
|
||||
# Debug/Development
|
||||
python3 train.py --data data/coco.data --img-size 320 --single-scale --batch-size 64 --accumulate 1 --epochs 1 --evolve --giou
|
||||
python3 test.py --weights weights/latest.pt --cfg cfg/yolov3-spp.cfg --img-size 320
|
||||
python3 test.py --weights weights/last.pt --cfg cfg/yolov3-spp.cfg --img-size 320
|
||||
|
||||
gsutil cp evolve.txt gs://ultralytics
|
||||
sudo shutdown
|
||||
|
|
|
|||
Loading…
Reference in New Issue