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ultralytics 8.0.26 new YOLOv5u models (#771)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Adrian Boguszewski <adrian.boguszewski@intel.com>
This commit is contained in:
Glenn Jocher
2023-02-02 21:11:23 +04:00
committed by GitHub
parent b83374b42d
commit fa8811dcee
23 changed files with 85 additions and 52 deletions
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7
ultralytics/yolo/cfg/__init__.py
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@ -148,7 +148,7 @@ def argument_error(arg):
return SyntaxError(f"'{arg}' is not a valid YOLO argument.\n{CLI_HELP_MSG}")
def entrypoint(debug=False):
def entrypoint(debug=''):
"""
This function is the ultralytics package entrypoint, it's responsible for parsing the command line arguments passed
to the package.
@ -163,7 +163,7 @@ def entrypoint(debug=False):
It uses the package's default cfg and initializes it using the passed overrides.
Then it calls the CLI function with the composed cfg
"""
args = ['train', 'model=yolov8n.pt', 'data=coco128.yaml', 'imgsz=32', 'epochs=1'] if debug else sys.argv[1:]
args = (debug.split(' ') if debug else sys.argv)[1:]
if not args: # no arguments passed
LOGGER.info(CLI_HELP_MSG)
return
@ -275,4 +275,5 @@ def copy_default_cfg():
if __name__ == '__main__':
entrypoint(debug=True)
# entrypoint(debug='yolo predict model=yolov8n.pt')
entrypoint(debug='')

22
ultralytics/yolo/data/dataloaders/stream_loaders.py
View File

@ -13,7 +13,7 @@ import cv2
import numpy as np
import requests
import torch
from PIL import Image, ImageOps
from PIL import Image
from ultralytics.yolo.data.augment import LetterBox
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
@ -50,7 +50,7 @@ class LoadStreams:
s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL
s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam
if s == 0 and (is_colab() or is_kaggle()):
raise NotImplementedError("'source=0' webcam not supported in Colab and Kaggle notebooks."
raise NotImplementedError("'source=0' webcam not supported in Colab and Kaggle notebooks. "
"Try running 'source=0' in a local environment.")
cap = cv2.VideoCapture(s)
if not cap.isOpened():
@ -61,9 +61,11 @@ class LoadStreams:
self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback
self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback
_, self.imgs[i] = cap.read() # guarantee first frame
success, self.imgs[i] = cap.read() # guarantee first frame
if not success or self.imgs[i] is None:
raise ConnectionError(f'{st}Failed to read images from {s}')
self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
LOGGER.info(f"{st}Success ✅ ({self.frames[i]} frames of shape {w}x{h} at {self.fps[i]:.2f} FPS)")
self.threads[i].start()
LOGGER.info('') # newline
@ -221,15 +223,15 @@ class LoadImages:
self.mode = 'video'
for _ in range(self.vid_stride):
self.cap.grab()
ret_val, im0 = self.cap.retrieve()
while not ret_val:
success, im0 = self.cap.retrieve()
while not success:
self.count += 1
self.cap.release()
if self.count == self.nf: # last video
raise StopIteration
path = self.files[self.count]
self._new_video(path)
ret_val, im0 = self.cap.read()
success, im0 = self.cap.read()
self.frame += 1
# im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False
@ -330,14 +332,14 @@ def autocast_list(source):
Merges a list of source of different types into a list of numpy arrays or PIL images
"""
files = []
for _, im in enumerate(source):
for im in source:
if isinstance(im, (str, Path)): # filename or uri
files.append(Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im))
elif isinstance(im, (Image.Image, np.ndarray)): # PIL or np Image
files.append(im)
else:
raise Exception(
"Unsupported type encountered! See docs for supported types https://docs.ultralytics.com/predict")
raise TypeError(f"type {type(im).__name__} is not a supported Ultralytics prediction source type. \n"
f"See https://docs.ultralytics.com/predict for supported source types.")
return files

22
ultralytics/yolo/data/scripts/download_weights.sh
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@ -1,22 +1,18 @@
#!/bin/bash
# Ultralytics YOLO 🚀, GPL-3.0 license
# Download latest models from https://github.com/ultralytics/yolov5/releases
# Example usage: bash data/scripts/download_weights.sh
# Download latest models from https://github.com/ultralytics/assets/releases
# Example usage: bash ultralytics/yolo/data/scripts/download_weights.sh
# parent
# └── yolov5
# ├── yolov5s.pt ← downloads here
# ├── yolov5m.pt
# └── weights
# ├── yolov8n.pt ← downloads here
# ├── yolov8s.pt
# └── ...
python - <<EOF
from utils.downloads import attempt_download
from ultralytics.yolo.utils.downloads import attempt_download_asset
p5 = list('nsmlx') # P5 models
p6 = [f'{x}6' for x in p5] # P6 models
cls = [f'{x}-cls' for x in p5] # classification models
seg = [f'{x}-seg' for x in p5] # classification models
for x in p5 + p6 + cls + seg:
attempt_download(f'weights/yolov5{x}.pt')
assets = [f'yolov8{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '-cls', '-seg')]
for x in assets:
attempt_download_asset(f'weights/{x}')
EOF

11
ultralytics/yolo/engine/exporter.py
View File

@ -319,15 +319,20 @@ class Exporter:
@try_export
def _export_openvino(self, prefix=colorstr('OpenVINO:')):
# YOLOv8 OpenVINO export
check_requirements('openvino-dev') # requires openvino-dev: https://pypi.org/project/openvino-dev/
check_requirements('openvino-dev>=2022.3') # requires openvino-dev: https://pypi.org/project/openvino-dev/
import openvino.runtime as ov # noqa
from openvino.tools import mo # noqa
LOGGER.info(f'\n{prefix} starting export with openvino {ov.__version__}...')
f = str(self.file).replace(self.file.suffix, f'_openvino_model{os.sep}')
f_onnx = self.file.with_suffix('.onnx')
f_ov = str(Path(f) / self.file.with_suffix('.xml').name)
cmd = f"mo --input_model {f_onnx} --output_dir {f} {'--compress_to_fp16' * self.args.half}"
subprocess.run(cmd.split(), check=True, env=os.environ) # export
ov_model = mo.convert_model(f_onnx,
model_name=self.pretty_name,
framework="onnx",
compress_to_fp16=self.args.half) # export
ov.serialize(ov_model, f_ov) # save
yaml_save(Path(f) / self.file.with_suffix('.yaml').name, self.metadata) # add metadata.yaml
return f, None

8
ultralytics/yolo/utils/checks.py
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@ -58,7 +58,13 @@ def check_imgsz(imgsz, stride=32, min_dim=1, floor=0):
stride = int(stride.max() if isinstance(stride, torch.Tensor) else stride)
# Convert image size to list if it is an integer
imgsz = [imgsz] if isinstance(imgsz, int) else list(imgsz)
if isinstance(imgsz, int):
imgsz = [imgsz]
elif isinstance(imgsz, (list, tuple)):
imgsz = list(imgsz)
else:
raise TypeError(f"'imgsz={imgsz}' is of invalid type {type(imgsz).__name__}. "
f"Valid imgsz types are int i.e. 'imgsz=640' or list i.e. 'imgsz=[640,640]'")
# Make image size a multiple of the stride
sz = [max(math.ceil(x / stride) * stride, floor) for x in imgsz]

18
ultralytics/yolo/utils/downloads.py
View File

@ -1,6 +1,7 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import contextlib
import re
import subprocess
from itertools import repeat
from multiprocessing.pool import ThreadPool
@ -118,7 +119,18 @@ def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'):
response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json() # github api
return response['tag_name'], [x['name'] for x in response['assets']] # tag, assets
file = Path(str(file).strip().replace("'", ''))
# YOLOv3/5u updates
file = str(file)
if 'yolov3' in file or 'yolov5' in file and 'u' not in file:
original_file = file
file = re.sub(r"(.*yolov5([nsmlx]))\.pt", "\\1u.pt", file) # i.e. yolov5n.pt -> yolov5nu.pt
file = re.sub(r"(.*yolov3(|-tiny|-spp))\.pt", "\\1u.pt", file) # i.e. yolov3-spp.pt -> yolov3-sppu.pt
if file != original_file:
LOGGER.info(f"PRO TIP 💡 Replace 'model={original_file}' with new 'model={file}'.\nYOLOv5 'u' models are "
f"trained with https://github.com/ultralytics/ultralytics and feature improved performance vs "
f"standard YOLOv5 models trained with https://github.com/ultralytics/yolov5.\n")
file = Path(file.strip().replace("'", ''))
if file.exists():
return str(file)
elif (SETTINGS['weights_dir'] / file).exists():
@ -136,7 +148,9 @@ def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'):
return file
# GitHub assets
assets = [f'yolov8{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')] # default
assets = [f'yolov8{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')] + \
[f'yolov5{size}u.pt' for size in 'nsmlx'] + \
[f'yolov3{size}u.pt' for size in ('', '-spp', '-tiny')]
try:
tag, assets = github_assets(repo, release)
except Exception:

6
ultralytics/yolo/v8/segment/predict.py
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@ -72,15 +72,11 @@ class SegmentationPredictor(DetectionPredictor):
im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(self.device).permute(2, 0, 1).flip(0).contiguous() /
255 if self.args.retina_masks else im[idx])
# Segments
if self.args.save_txt:
segments = mask.segments
# Write results
for j, d in enumerate(reversed(det)):
cls, conf = d.cls.squeeze(), d.conf.squeeze()
if self.args.save_txt: # Write to file
seg = segments[j].copy()
seg = mask.segments[len(det) - j - 1].copy() # reversed mask.segments
seg = seg.reshape(-1) # (n,2) to (n*2)
line = (cls, *seg, conf) if self.args.save_conf else (cls, *seg) # label format
with open(f'{self.txt_path}.txt', 'a') as f: