Unverified Commit 0ad6301c authored by Glenn Jocher's avatar Glenn Jocher Committed by GitHub
Browse files

Update script headers (#4163)

* Update download script headers

* cleanup

* bug fix attempt

* bug fix attempt2

* bug fix attempt3

* cleanup
parent f8e11483
#!/bin/bash
# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
# Download latest models from https://github.com/ultralytics/yolov5/releases
# Usage:
# $ bash path/to/download_weights.sh
# YOLOv5 🚀 example usage: bash path/to/download_weights.sh
# parent
# └── yolov5
# ├── yolov5s.pt ← downloads here
# ├── yolov5m.pt
# └── ...
python - <<EOF
from utils.google_utils import attempt_download
......
#!/bin/bash
# COCO 2017 dataset http://cocodataset.org
# Download command: bash data/scripts/get_coco.sh
# Train command: python train.py --data coco.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /coco
# /yolov5
# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
# Download COCO 2017 dataset http://cocodataset.org
# YOLOv5 🚀 example usage: bash data/scripts/get_coco.sh
# parent
# ├── yolov5
# └── datasets
# └── coco ← downloads here
# Download/unzip labels
d='../datasets' # unzip directory
......
#!/bin/bash
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128
# Download command: bash data/scripts/get_coco128.sh
# Train command: python train.py --data coco128.yaml
# Default dataset location is next to /yolov5:
# /parent_folder
# /coco128
# /yolov5
# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
# YOLOv5 🚀 example usage: bash data/scripts/get_coco128.sh
# parent
# ├── yolov5
# └── datasets
# └── coco128 ← downloads here
# Download/unzip images and labels
d='../' # unzip directory
d='../datasets' # unzip directory
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
f='coco128.zip' # or 'coco2017labels-segments.zip', 68 MB
echo 'Downloading' $url$f ' ...'
......
......@@ -78,8 +78,9 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
plots = not evolve # create plots
cuda = device.type != 'cpu'
init_seeds(1 + RANK)
with open(data) as f:
data_dict = yaml.safe_load(f) # data dict
with open(data, encoding='ascii', errors='ignore') as f:
data_dict = yaml.safe_load(f)
nc = 1 if single_cls else int(data_dict['nc']) # number of classes
names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}' # check
......
......@@ -60,11 +60,11 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
print('') # newline
def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
""" Creates kmeans-evolved anchors from training dataset
Arguments:
path: path to dataset *.yaml, or a loaded dataset
dataset: path to data.yaml, or a loaded dataset
n: number of anchors
img_size: image size used for training
thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
......@@ -103,13 +103,11 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg
return k
if isinstance(path, str): # *.yaml file
with open(path) as f:
if isinstance(dataset, str): # *.yaml file
with open(dataset, encoding='ascii', errors='ignore') as f:
data_dict = yaml.safe_load(f) # model dict
from utils.datasets import LoadImagesAndLabels
dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
else:
dataset = path # dataset
# Get label wh
shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
......
......@@ -909,7 +909,7 @@ def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False):
return False, None, path
zipped, data_dir, yaml_path = unzip(Path(path))
with open(check_file(yaml_path)) as f:
with open(check_file(yaml_path), encoding='ascii', errors='ignore') as f:
data = yaml.safe_load(f) # data dict
if zipped:
data['path'] = data_dir # TODO: should this be dir.resolve()?
......
......@@ -8,9 +8,9 @@ WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
def create_dataset_artifact(opt):
with open(opt.data) as f:
with open(opt.data, encoding='ascii', errors='ignore') as f:
data = yaml.safe_load(f) # data dict
logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation')
logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation') # TODO: return value unused
if __name__ == '__main__':
......
......@@ -62,7 +62,7 @@ def check_wandb_resume(opt):
def process_wandb_config_ddp_mode(opt):
with open(check_file(opt.data)) as f:
with open(check_file(opt.data), encoding='ascii', errors='ignore') as f:
data_dict = yaml.safe_load(f) # data dict
train_dir, val_dir = None, None
if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):
......@@ -150,7 +150,7 @@ class WandbLogger():
opt.single_cls,
'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
print("Created dataset config file ", config_path)
with open(config_path) as f:
with open(config_path, encoding='ascii', errors='ignore') as f:
wandb_data_dict = yaml.safe_load(f)
return wandb_data_dict
......@@ -226,7 +226,7 @@ class WandbLogger():
print("Saving model artifact on epoch ", epoch + 1)
def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
with open(data_file) as f:
with open(data_file, encoding='ascii', errors='ignore') as f:
data = yaml.safe_load(f) # data dict
check_dataset(data)
nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
......
......@@ -123,7 +123,7 @@ def run(data,
# model = nn.DataParallel(model)
# Data
with open(data) as f:
with open(data, encoding='ascii', errors='ignore') as f:
data = yaml.safe_load(f)
check_dataset(data) # check
......
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment