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`ultralytics 8.0.131` NCNN, HUB and FastSAM fixes (#3587)

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pull/3603/head v8.0.131
Glenn Jocher 1 year ago committed by GitHub
parent 23c7cd4c9f
commit d9db6cd42d
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11 changed files with 24 additions and 23 deletions
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  1. 2
      docs/modes/export.md
  2. 2
      docs/tasks/classify.md
  3. 2
      docs/tasks/detect.md
  4. 2
      docs/tasks/pose.md
  5. 2
      docs/tasks/segment.md
  6. 4
      ultralytics/__init__.py
  7. 11
      ultralytics/hub/utils.py
  8. 10
      ultralytics/yolo/engine/exporter.py
  9. 2
      ultralytics/yolo/fastsam/model.py
  10. 9
      ultralytics/yolo/fastsam/utils.py
  11. 1
      ultralytics/yolo/utils/__init__.py

2
docs/modes/export.md
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@ -85,4 +85,4 @@ i.e. `format='onnx'` or `format='engine'`.
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half` |

2
docs/tasks/classify.md
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@ -176,6 +176,6 @@ i.e. `yolo predict model=yolov8n-cls.onnx`. Usage examples are shown for your mo
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-cls_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-cls_web_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-cls_ncnn_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-cls_ncnn_model/` | ✅ | `imgsz`, `half` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

2
docs/tasks/detect.md
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@ -167,6 +167,6 @@ Available YOLOv8 export formats are in the table below. You can predict or valid
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

2
docs/tasks/pose.md
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@ -181,6 +181,6 @@ i.e. `yolo predict model=yolov8n-pose.onnx`. Usage examples are shown for your m
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-pose_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-pose_web_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-pose_ncnn_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-pose_ncnn_model/` | ✅ | `imgsz`, `half` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

2
docs/tasks/segment.md
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@ -181,6 +181,6 @@ i.e. `yolo predict model=yolov8n-seg.onnx`. Usage examples are shown for your mo
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-seg_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-seg_web_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-seg_ncnn_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-seg_ncnn_model/` | ✅ | `imgsz`, `half` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

4
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = '8.0.130'
__version__ = '8.0.131'
from ultralytics.hub import start
from ultralytics.vit.rtdetr import RTDETR
@ -11,4 +11,4 @@ from ultralytics.yolo.nas import NAS
from ultralytics.yolo.utils.checks import check_yolo as checks
from ultralytics.yolo.utils.downloads import download
__all__ = '__version__', 'YOLO', 'NAS', 'SAM', 'RTDETR', 'checks', 'start', 'download', 'FastSAM' # allow simpler import
__all__ = '__version__', 'YOLO', 'NAS', 'SAM', 'FastSAM', 'RTDETR', 'checks', 'download', 'start' # allow simpler import

11
ultralytics/hub/utils.py
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@ -78,10 +78,13 @@ def requests_with_progress(method, url, **kwargs):
return requests.request(method, url, **kwargs)
response = requests.request(method, url, stream=True, **kwargs)
total = int(response.headers.get('content-length', 0)) # total size
pbar = tqdm(total=total, unit='B', unit_scale=True, unit_divisor=1024, bar_format=TQDM_BAR_FORMAT)
for data in response.iter_content(chunk_size=1024):
pbar.update(len(data))
pbar.close()
try:
pbar = tqdm(total=total, unit='B', unit_scale=True, unit_divisor=1024, bar_format=TQDM_BAR_FORMAT)
for data in response.iter_content(chunk_size=1024):
pbar.update(len(data))
pbar.close()
except requests.exceptions.ChunkedEncodingError: # avoid 'Connection broken: IncompleteRead' warnings
response.close()
return response

10
ultralytics/yolo/engine/exporter.py
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@ -50,7 +50,6 @@ TensorFlow.js:
"""
import json
import os
import platform
import shutil
import subprocess
import time
@ -64,7 +63,7 @@ from ultralytics.nn.autobackend import check_class_names
from ultralytics.nn.modules import C2f, Detect, RTDETRDecoder
from ultralytics.nn.tasks import DetectionModel, SegmentationModel
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.utils import (DEFAULT_CFG, LINUX, LOGGER, MACOS, ROOT, __version__, callbacks, colorstr,
from ultralytics.yolo.utils import (ARM64, DEFAULT_CFG, LINUX, LOGGER, MACOS, ROOT, __version__, callbacks, colorstr,
get_default_args, yaml_save)
from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version
from ultralytics.yolo.utils.downloads import attempt_download_asset, get_github_assets
@ -72,8 +71,6 @@ from ultralytics.yolo.utils.files import file_size
from ultralytics.yolo.utils.ops import Profile
from ultralytics.yolo.utils.torch_utils import get_latest_opset, select_device, smart_inference_mode
ARM64 = platform.machine() in ('arm64', 'aarch64')
def export_formats():
"""YOLOv8 export formats."""
@ -170,7 +167,8 @@ class Exporter:
assert not self.args.dynamic, 'half=True not compatible with dynamic=True, i.e. use only one.'
self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2) # check image size
if self.args.optimize:
assert self.device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
assert not ncnn, "optimize=True not compatible with format='ncnn', i.e. use optimize=False"
assert self.device.type == 'cpu', "optimize=True not compatible with cuda devices, i.e. use device='cpu'"
if edgetpu and not LINUX:
raise SystemError('Edge TPU export only supported on Linux. See https://coral.ai/docs/edgetpu/compiler/')
@ -405,7 +403,7 @@ class Exporter:
"""
YOLOv8 NCNN export using PNNX https://github.com/pnnx/pnnx.
"""
check_requirements('ncnn') # requires NCNN
check_requirements('git+https://github.com/Tencent/ncnn.git' if ARM64 else 'ncnn') # requires NCNN
import ncnn # noqa
LOGGER.info(f'\n{prefix} starting export with NCNN {ncnn.__version__}...')

2
ultralytics/yolo/fastsam/model.py
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@ -22,7 +22,7 @@ from .predict import FastSAMPredictor
class FastSAM(YOLO):
def __init__(self, model='FastSAM-x.pt'):
# Call the __init__ method of the parent class (YOLO) with the updated default model
"""Call the __init__ method of the parent class (YOLO) with the updated default model"""
if model == 'FastSAM.pt':
model = 'FastSAM-x.pt'
super().__init__(model=model)

9
ultralytics/yolo/fastsam/utils.py
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@ -20,11 +20,10 @@ def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20):
h, w = image_shape
# Adjust boxes
boxes[:, 0] = torch.where(boxes[:, 0] < threshold, 0, boxes[:, 0]) # x1
boxes[:, 1] = torch.where(boxes[:, 1] < threshold, 0, boxes[:, 1]) # y1
boxes[:, 2] = torch.where(boxes[:, 2] > w - threshold, w, boxes[:, 2]) # x2
boxes[:, 3] = torch.where(boxes[:, 3] > h - threshold, h, boxes[:, 3]) # y2
boxes[boxes[:, 0] < threshold, 0] = 0 # x1
boxes[boxes[:, 1] < threshold, 1] = 0 # y1
boxes[boxes[:, 2] > w - threshold, 2] = w # x2
boxes[boxes[:, 3] > h - threshold, 3] = h # y2
return boxes

1
ultralytics/yolo/utils/__init__.py
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@ -38,6 +38,7 @@ VERBOSE = str(os.getenv('YOLO_VERBOSE', True)).lower() == 'true' # global verbo
TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}' # tqdm bar format
LOGGING_NAME = 'ultralytics'
MACOS, LINUX, WINDOWS = (platform.system() == x for x in ['Darwin', 'Linux', 'Windows']) # environment booleans
ARM64 = platform.machine() in ('arm64', 'aarch64') # ARM64 booleans
HELP_MSG = \
"""
Usage examples for running YOLOv8:

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