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ultralytics 8.0.48 Edge TPU fix and Metrics updates (#1171)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: majid nasiri <majnasai@gmail.com>
This commit is contained in:
Glenn Jocher
2023-02-27 21:34:22 -08:00
committed by GitHub
parent a58f766f94
commit 74e4c94806
23 changed files with 426 additions and 245 deletions
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19
ultralytics/yolo/engine/exporter.py
View File

@ -243,15 +243,12 @@ class Exporter:
if coreml: # CoreML
f[4], _ = self._export_coreml()
if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats
LOGGER.warning('WARNING ⚠️ YOLOv8 TensorFlow export is still under development. '
'Please consider contributing to the effort if you have TF expertise. Thank you!')
nms = False
self.args.int8 |= edgetpu
f[5], s_model = self._export_saved_model()
if pb or tfjs: # pb prerequisite to tfjs
f[6], _ = self._export_pb(s_model)
if tflite:
f[7], _ = self._export_tflite(s_model, nms=nms, agnostic_nms=self.args.agnostic_nms)
f[7], _ = self._export_tflite(s_model, nms=False, agnostic_nms=self.args.agnostic_nms)
if edgetpu:
f[8], _ = self._export_edgetpu(tflite_model=str(
Path(f[5]) / (self.file.stem + '_full_integer_quant.tflite'))) # int8 in/out
@ -619,20 +616,18 @@ class Exporter:
@try_export
def _export_edgetpu(self, tflite_model='', prefix=colorstr('Edge TPU:')):
# YOLOv8 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
LOGGER.warning(f'{prefix} WARNING ⚠️ Edge TPU known bug https://github.com/ultralytics/ultralytics/issues/1185')
cmd = 'edgetpu_compiler --version'
help_url = 'https://coral.ai/docs/edgetpu/compiler/'
assert LINUX, f'export only supported on Linux. See {help_url}'
if subprocess.run(f'{cmd} > /dev/null', shell=True).returncode != 0:
if subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, shell=True).returncode != 0:
LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system
for c in (
# 'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -', # errors
'wget --no-check-certificate -q -O - https://packages.cloud.google.com/apt/doc/apt-key.gpg | '
'sudo apt-key add -',
'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | ' # no comma
'sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
'sudo apt-get update',
'sudo apt-get install edgetpu-compiler'):
'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'):
subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]

23
ultralytics/yolo/engine/model.py
View File

@ -43,7 +43,7 @@ class YOLO:
cfg (str): The model configuration if loaded from *.yaml file.
ckpt_path (str): The checkpoint file path.
overrides (dict): Overrides for the trainer object.
metrics_data (Any): The data for metrics.
metrics (Any): The data for metrics.
Methods:
__call__(source=None, stream=False, **kwargs):
@ -67,7 +67,7 @@ class YOLO:
list(ultralytics.yolo.engine.results.Results): The prediction results.
"""
def __init__(self, model='yolov8n.pt', task=None) -> None:
def __init__(self, model='yolov8n.pt', task=None, session=None) -> None:
"""
Initializes the YOLO model.
@ -83,7 +83,8 @@ class YOLO:
self.cfg = None # if loaded from *.yaml
self.ckpt_path = None
self.overrides = {} # overrides for trainer object
self.metrics_data = None
self.metrics = None # validation/training metrics
self.session = session # HUB session
# Load or create new YOLO model
suffix = Path(model).suffix
@ -184,6 +185,7 @@ class YOLO:
self._check_is_pytorch_model()
self.model.fuse()
@smart_inference_mode()
def predict(self, source=None, stream=False, **kwargs):
"""
Perform prediction using the YOLO model.
@ -217,7 +219,6 @@ class YOLO:
is_cli = sys.argv[0].endswith('yolo') or sys.argv[0].endswith('ultralytics')
return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
@smart_inference_mode()
def track(self, source=None, stream=False, **kwargs):
from ultralytics.tracker import register_tracker
register_tracker(self)
@ -252,7 +253,7 @@ class YOLO:
validator = TASK_MAP[self.task][2](args=args)
validator(model=self.model)
self.metrics_data = validator.metrics
self.metrics = validator.metrics
return validator.metrics
@ -314,12 +315,13 @@ class YOLO:
if not overrides.get('resume'): # manually set model only if not resuming
self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
self.model = self.trainer.model
self.trainer.hub_session = self.session # attach optional HUB session
self.trainer.train()
# update model and cfg after training
if RANK in {0, -1}:
self.model, _ = attempt_load_one_weight(str(self.trainer.best))
self.overrides = self.model.args
self.metrics_data = getattr(self.trainer.validator, 'metrics', None) # TODO: no metrics returned by DDP
self.metrics = getattr(self.trainer.validator, 'metrics', None) # TODO: no metrics returned by DDP
def to(self, device):
"""
@ -352,15 +354,6 @@ class YOLO:
"""
return self.model.transforms if hasattr(self.model, 'transforms') else None
@property
def metrics(self):
"""
Returns metrics if computed
"""
if not self.metrics_data:
LOGGER.info('No metrics data found! Run training or validation operation first.')
return self.metrics_data
@staticmethod
def add_callback(event: str, func):
"""

3
ultralytics/yolo/engine/results.py
View File

@ -139,7 +139,8 @@ class Results:
annotator.masks(masks.data, colors=[colors(x, True) for x in boxes.cls], im_gpu=im)
if logits is not None:
top5i = logits.argsort(0, descending=True)[:5].tolist() # top 5 indices
n5 = min(len(self.names), 5)
top5i = logits.argsort(0, descending=True)[:n5].tolist() # top 5 indices
text = f"{', '.join(f'{names[j] if names else j} {logits[j]:.2f}' for j in top5i)}, "
annotator.text((32, 32), text, txt_color=(255, 255, 255)) # TODO: allow setting colors