diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index d739fed..53bdf9d 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -38,7 +38,7 @@ jobs:
if [ "${{ matrix.os }}" == "macos-latest" ]; then
pip install -e . coremltools openvino-dev tensorflow-macos --extra-index-url https://download.pytorch.org/whl/cpu
else
- pip install -e . coremltools openvino-dev tensorflow-cpu paddlepaddle x2paddle --extra-index-url https://download.pytorch.org/whl/cpu
+ pip install -e . coremltools openvino-dev tensorflow-cpu --extra-index-url https://download.pytorch.org/whl/cpu
fi
yolo export format=tflite
- name: Check environment
@@ -66,7 +66,7 @@ jobs:
shell: python
run: |
from ultralytics.yolo.utils.benchmarks import benchmark
- benchmark(model='${{ matrix.model }}-cls.pt', imgsz=160, half=False, hard_fail=0.70)
+ benchmark(model='${{ matrix.model }}-cls.pt', imgsz=160, half=False, hard_fail=0.60)
- name: Benchmark Summary
run: cat benchmarks.log
diff --git a/README.zh-CN.md b/README.zh-CN.md
index 13aede8..355c281 100644
--- a/README.zh-CN.md
+++ b/README.zh-CN.md
@@ -223,10 +223,11 @@ Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自
## License
-- YOLOv8 在两种不同的 License 下可用：
- - **GPL-3.0 License**： 查看 [License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件的详细信息。
- - **企业License**：在没有 GPL-3.0 开源要求的情况下为商业产品开发提供更大的灵活性。典型用例是将 Ultralytics 软件和 AI
- 模型嵌入到商业产品和应用程序中。在以下位置申请企业许可证 [Ultralytics 许可](https://ultralytics.com/license) 。
+YOLOv8 在两种不同的 License 下可用：
+
+- **GPL-3.0 License**： 查看 [License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件的详细信息。
+- **企业License**：在没有 GPL-3.0 开源要求的情况下为商业产品开发提供更大的灵活性。典型用例是将 Ultralytics 软件和 AI
+ 模型嵌入到商业产品和应用程序中。在以下位置申请企业许可证 [Ultralytics 许可](https://ultralytics.com/license) 。
## 联系我们
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 89964c1..345c882 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -29,9 +29,8 @@ WORKDIR /usr/src/ultralytics
RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
# Install pip packages
-COPY requirements.txt .
RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache ultralytics[export] albumentations comet gsutil notebook
+RUN pip install --no-cache '.[export]' albumentations comet gsutil notebook
# Set environment variables
ENV OMP_NUM_THREADS=1
diff --git a/docker/Dockerfile-arm64 b/docker/Dockerfile-arm64
index 6499f38..82ff4a9 100644
--- a/docker/Dockerfile-arm64
+++ b/docker/Dockerfile-arm64
@@ -24,9 +24,8 @@ WORKDIR /usr/src/ultralytics
RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
# Install pip packages
-COPY requirements.txt .
RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache ultralytics albumentations gsutil notebook
+RUN pip install --no-cache . albumentations gsutil notebook
# Cleanup
ENV DEBIAN_FRONTEND teletype
diff --git a/docker/Dockerfile-cpu b/docker/Dockerfile-cpu
index 2a93c55..364aaf4 100644
--- a/docker/Dockerfile-cpu
+++ b/docker/Dockerfile-cpu
@@ -24,9 +24,8 @@ WORKDIR /usr/src/ultralytics
RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
# Install pip packages
-COPY requirements.txt .
RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache ultralytics[export] albumentations gsutil notebook \
+RUN pip install --no-cache '.[export]' albumentations gsutil notebook \
--extra-index-url https://download.pytorch.org/whl/cpu
# Cleanup
diff --git a/docs/predict.md b/docs/predict.md
index 966af3f..ef8dd0a 100644
--- a/docs/predict.md
+++ b/docs/predict.md
@@ -97,8 +97,8 @@ Class reference documentation for `Results` module and its components can be fou
## Plotting results
-You can use `plot()` function of `Result` object to plot results on in image object. It plots all components(boxes, masks,
-classification logits, etc) found in the results object
+You can use `plot()` function of `Result` object to plot results on in image object. It plots all components(boxes,
+masks, classification logits, etc) found in the results object
```python
res = model(img)
diff --git a/docs/tasks/tracking.md b/docs/tasks/tracking.md
index 259487b..81734fe 100644
--- a/docs/tasks/tracking.md
+++ b/docs/tasks/tracking.md
@@ -42,11 +42,15 @@ Use a trained YOLOv8n/YOLOv8n-seg model to run tracker on video streams.
```
-As in the above usage, we support both the detection and segmentation models for tracking and the only thing you need to do is loading the corresponding(detection or segmentation) model.
+As in the above usage, we support both the detection and segmentation models for tracking and the only thing you need to
+do is loading the corresponding (detection or segmentation) model.
## Configuration
+
### Tracking
-Tracking shares the configuration with predict, i.e `conf`, `iou`, `show`. More configurations please refer to [predict page](https://docs.ultralytics.com/cfg/#prediction).
+
+Tracking shares the configuration with predict, i.e `conf`, `iou`, `show`. More configurations please refer
+to [predict page](https://docs.ultralytics.com/cfg/#prediction).
!!! example ""
=== "Python"
@@ -65,7 +69,10 @@ Tracking shares the configuration with predict, i.e `conf`, `iou`, `show`. More
```
### Tracker
-We also support using a modified tracker config file, just copy a config file i.e `custom_tracker.yaml` from [ultralytics/tracker/cfg](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/tracker/cfg) and modify any configurations(expect the `tracker_type`) you need to.
+
+We also support using a modified tracker config file, just copy a config file i.e `custom_tracker.yaml`
+from [ultralytics/tracker/cfg](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/tracker/cfg) and modify
+any configurations(expect the `tracker_type`) you need to.
!!! example ""
=== "Python"
@@ -82,5 +89,7 @@ We also support using a modified tracker config file, just copy a config file i.
yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" tracker='custom_tracker.yaml'
```
-Please refer to [ultralytics/tracker/cfg](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/tracker/cfg) page.
+
+Please refer to [ultralytics/tracker/cfg](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/tracker/cfg)
+page.
diff --git a/requirements.txt b/requirements.txt
index 2a3010f..eb4b7fe 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -37,7 +37,6 @@ seaborn>=0.11.0
# Extras --------------------------------------
psutil # system utilization
thop>=0.1.1 # FLOPs computation
-wheel>=0.38.0 # Snyk vulnerability fix
# ipython # interactive notebook
# albumentations>=1.0.3
# pycocotools>=2.0.6 # COCO mAP
diff --git a/setup.cfg b/setup.cfg
index fcc17e1..2cde6a4 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -25,17 +25,19 @@ verbose = 2
# https://pep8.readthedocs.io/en/latest/intro.html#error-codes
format = pylint
# see: https://www.flake8rules.com/
-ignore = E731,F405,E402,F401,W504,E127,E231,E501,F403
+ignore = E731,F405,E402,W504,E501
# E731: Do not assign a lambda expression, use a def
# F405: name may be undefined, or defined from star imports: module
# E402: module level import not at top of file
- # F401: module imported but unused
# W504: line break after binary operator
- # E127: continuation line over-indented for visual indent
- # E231: missing whitespace after ‘,’, ‘;’, or ‘:’
# E501: line too long
+ # removed:
+ # F401: module imported but unused
+ # E231: missing whitespace after ‘,’, ‘;’, or ‘:’
+ # E127: continuation line over-indented for visual indent
# F403: ‘from module import *’ used; unable to detect undefined names
+
[isort]
# https://pycqa.github.io/isort/docs/configuration/options.html
line_length = 120
@@ -48,7 +50,7 @@ spaces_before_comment = 2
COLUMN_LIMIT = 120
COALESCE_BRACKETS = True
SPACES_AROUND_POWER_OPERATOR = True
-SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = False
+SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = True
SPLIT_BEFORE_CLOSING_BRACKET = False
SPLIT_BEFORE_FIRST_ARGUMENT = False
# EACH_DICT_ENTRY_ON_SEPARATE_LINE = False
diff --git a/setup.py b/setup.py
index 6ba9fc0..ba0296e 100644
--- a/setup.py
+++ b/setup.py
@@ -59,7 +59,7 @@ setup(
'Topic :: Scientific/Engineering :: Image Recognition',
'Operating System :: POSIX :: Linux',
'Operating System :: MacOS',
- 'Operating System :: Microsoft :: Windows',],
+ 'Operating System :: Microsoft :: Windows', ],
keywords='machine-learning, deep-learning, vision, ML, DL, AI, YOLO, YOLOv3, YOLOv5, YOLOv8, HUB, Ultralytics',
entry_points={
'console_scripts': ['yolo = ultralytics.yolo.cfg:entrypoint', 'ultralytics = ultralytics.yolo.cfg:entrypoint']})
diff --git a/tests/test_cli.py b/tests/test_cli.py
index 2879b2a..9b01ce2 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -22,7 +22,7 @@ def test_special_modes():
# Train checks ---------------------------------------------------------------------------------------------------------
def test_train_det():
- run(f'yolo train detect model={CFG}.yaml data=coco8.yaml imgsz=32 epochs=1')
+ run(f'yolo train detect model={CFG}.yaml data=coco8.yaml imgsz=32 epochs=1 v5loader')
def test_train_seg():
@@ -48,7 +48,7 @@ def test_val_classify():
# Predict checks -------------------------------------------------------------------------------------------------------
def test_predict_detect():
- run(f"yolo predict model={MODEL}.pt source={ROOT / 'assets'} imgsz=32 save")
+ run(f"yolo predict model={MODEL}.pt source={ROOT / 'assets'} imgsz=32 save save_crop save_txt")
if checks.check_online():
run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32')
run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=32')
diff --git a/tests/test_python.py b/tests/test_python.py
index 3ae8077..ad8c314 100644
--- a/tests/test_python.py
+++ b/tests/test_python.py
@@ -162,9 +162,8 @@ def test_workflow():
def test_predict_callback_and_setup():
-
- def on_predict_batch_end(predictor):
- # results -> List[batch_size]
+ # test callback addition for prediction
+ def on_predict_batch_end(predictor): # results -> List[batch_size]
path, _, im0s, _, _ = predictor.batch
# print('on_predict_batch_end', im0s[0].shape)
im0s = im0s if isinstance(im0s, list) else [im0s]
diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index 1f37bbc..f6b125a 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,8 +1,8 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
-__version__ = '8.0.46'
+__version__ = '8.0.47'
from ultralytics.yolo.engine.model import YOLO
from ultralytics.yolo.utils.checks import check_yolo as checks
-__all__ = ['__version__', 'YOLO', 'checks'] # allow simpler import
+__all__ = '__version__', 'YOLO', 'checks' # allow simpler import
diff --git a/ultralytics/hub/utils.py b/ultralytics/hub/utils.py
index 8a2bc49..e0a7daa 100644
--- a/ultralytics/hub/utils.py
+++ b/ultralytics/hub/utils.py
@@ -154,11 +154,12 @@ class Traces:
'python': platform.python_version(),
'release': __version__,
'environment': ENVIRONMENT}
- self.enabled = SETTINGS['sync'] and \
- RANK in {-1, 0} and \
- check_online() and \
- not TESTS_RUNNING and \
- (is_pip_package() or get_git_origin_url() == 'https://github.com/ultralytics/ultralytics.git')
+ self.enabled = \
+ SETTINGS['sync'] and \
+ RANK in {-1, 0} and \
+ check_online() and \
+ not TESTS_RUNNING and \
+ (is_pip_package() or get_git_origin_url() == 'https://github.com/ultralytics/ultralytics.git')
def __call__(self, cfg, all_keys=False, traces_sample_rate=1.0):
"""
diff --git a/ultralytics/nn/autobackend.py b/ultralytics/nn/autobackend.py
index 7656859..3f05258 100644
--- a/ultralytics/nn/autobackend.py
+++ b/ultralytics/nn/autobackend.py
@@ -136,7 +136,7 @@ class AutoBackend(nn.Module):
batch_dim = get_batch(network)
if batch_dim.is_static:
batch_size = batch_dim.get_length()
- executable_network = ie.compile_model(network, device_name='CPU') # device_name="MYRIAD" for Intel NCS2
+ executable_network = ie.compile_model(network, device_name='CPU') # device_name="MYRIAD" for NCS2
elif engine: # TensorRT
LOGGER.info(f'Loading {w} for TensorRT inference...')
import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download
@@ -176,6 +176,8 @@ class AutoBackend(nn.Module):
LOGGER.info(f'Loading {w} for CoreML inference...')
import coremltools as ct
model = ct.models.MLModel(w)
+ names, stride, task = (model.user_defined_metadata.get(k) for k in ('names', 'stride', 'task'))
+ names, stride = eval(names), int(stride)
elif saved_model: # TF SavedModel
LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
import tensorflow as tf
@@ -185,18 +187,13 @@ class AutoBackend(nn.Module):
LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
import tensorflow as tf
+ from ultralytics.yolo.engine.exporter import gd_outputs
+
def wrap_frozen_graph(gd, inputs, outputs):
x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=''), []) # wrapped
ge = x.graph.as_graph_element
return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
- def gd_outputs(gd):
- name_list, input_list = [], []
- for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef
- name_list.append(node.name)
- input_list.extend(node.input)
- return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
-
gd = tf.Graph().as_graph_def() # TF GraphDef
with open(w, 'rb') as f:
gd.ParseFromString(f.read())
@@ -319,10 +316,17 @@ class AutoBackend(nn.Module):
self.context.execute_v2(list(self.binding_addrs.values()))
y = [self.bindings[x].data for x in sorted(self.output_names)]
elif self.coreml: # CoreML
- im = im.cpu().numpy()
- im = Image.fromarray((im[0] * 255).astype('uint8'))
+ im = im[0].cpu().numpy()
+ if self.task == 'classify':
+ from ultralytics.yolo.data.utils import IMAGENET_MEAN, IMAGENET_STD
+
+ # im_pil = Image.fromarray(((im / 6 + 0.5) * 255).astype('uint8'))
+ for i in range(3):
+ im[..., i] *= IMAGENET_STD[i]
+ im[..., i] += IMAGENET_MEAN[i]
+ im_pil = Image.fromarray((im * 255).astype('uint8'))
# im = im.resize((192, 320), Image.ANTIALIAS)
- y = self.model.predict({'image': im}) # coordinates are xywh normalized
+ y = self.model.predict({'image': im_pil}) # coordinates are xywh normalized
if 'confidence' in y:
box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
diff --git a/ultralytics/nn/tasks.py b/ultralytics/nn/tasks.py
index 9c09d61..7104f9d 100644
--- a/ultralytics/nn/tasks.py
+++ b/ultralytics/nn/tasks.py
@@ -11,7 +11,7 @@ import torch.nn as nn
from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify,
Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus,
GhostBottleneck, GhostConv, Segment)
-from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, colorstr, yaml_load
+from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, colorstr, emojis, yaml_load
from ultralytics.yolo.utils.checks import check_requirements, check_yaml
from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, fuse_deconv_and_bn, initialize_weights,
intersect_dicts, make_divisible, model_info, scale_img, time_sync)
@@ -76,7 +76,7 @@ class BaseModel(nn.Module):
None
"""
c = m == self.model[-1] # is final layer, copy input as inplace fix
- o = thop.profile(m, inputs=(x.clone() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
+ o = thop.profile(m, inputs=[x.clone() if c else x], verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
t = time_sync()
for _ in range(10):
m(x.clone() if c else x)
@@ -339,14 +339,20 @@ def torch_safe_load(weight):
file = attempt_download_asset(weight) # search online if missing locally
try:
return torch.load(file, map_location='cpu'), file # load
- except ModuleNotFoundError as e:
- if e.name == 'omegaconf': # e.name is missing module name
- LOGGER.warning(f'WARNING ⚠️ {weight} requires {e.name}, which is not in ultralytics requirements.'
- f'\nAutoInstall will run now for {e.name} but this feature will be removed in the future.'
- f'\nRecommend fixes are to train a new model using updated ultralytics package or to '
- f'download updated models from https://github.com/ultralytics/assets/releases/tag/v0.0.0')
- if e.name != 'models':
- check_requirements(e.name) # install missing module
+ except ModuleNotFoundError as e: # e.name is missing module name
+ if e.name == 'models':
+ raise TypeError(
+ emojis(f'ERROR ❌️ {weight} appears to be an Ultralytics YOLOv5 model originally trained '
+ f'with https://github.com/ultralytics/yolov5.\nThis model is NOT forwards compatible with '
+ f'YOLOv8 at https://github.com/ultralytics/ultralytics.'
+ f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
+ f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'")) from e
+ LOGGER.warning(f"WARNING ⚠️ {weight} appears to require '{e.name}', which is not in ultralytics requirements."
+ f"\nAutoInstall will run now for '{e.name}' but this feature will be removed in the future."
+ f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
+ f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'")
+ check_requirements(e.name) # install missing module
+
return torch.load(file, map_location='cpu'), file # load
@@ -437,22 +443,21 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
args[j] = eval(a) if isinstance(a, str) else a # eval strings
n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
- if m in {
- Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
- BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
+ if m in (Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
+ BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x):
c1, c2 = ch[f], args[0]
if c2 != nc: # if c2 not equal to number of classes (i.e. for Classify() output)
c2 = make_divisible(c2 * gw, 8)
args = [c1, c2, *args[1:]]
- if m in {BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x}:
+ if m in (BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x):
args.insert(2, n) # number of repeats
n = 1
elif m is nn.BatchNorm2d:
args = [ch[f]]
elif m is Concat:
c2 = sum(ch[x] for x in f)
- elif m in {Detect, Segment}:
+ elif m in (Detect, Segment):
args.append([ch[x] for x in f])
if m is Segment:
args[2] = make_divisible(args[2] * gw, 8)
@@ -490,11 +495,11 @@ def guess_model_task(model):
def cfg2task(cfg):
# Guess from YAML dictionary
m = cfg['head'][-1][-2].lower() # output module name
- if m in ['classify', 'classifier', 'cls', 'fc']:
+ if m in ('classify', 'classifier', 'cls', 'fc'):
return 'classify'
- if m in ['detect']:
+ if m == 'detect':
return 'detect'
- if m in ['segment']:
+ if m == 'segment':
return 'segment'
# Guess from model cfg
diff --git a/ultralytics/tracker/__init__.py b/ultralytics/tracker/__init__.py
index 7f4e335..85bf24e 100644
--- a/ultralytics/tracker/__init__.py
+++ b/ultralytics/tracker/__init__.py
@@ -2,3 +2,5 @@
from .track import register_tracker
from .trackers import BOTSORT, BYTETracker
+
+__all__ = 'register_tracker', 'BOTSORT', 'BYTETracker' # allow simpler import
diff --git a/ultralytics/tracker/trackers/__init__.py b/ultralytics/tracker/trackers/__init__.py
index ae99ab6..10f1cf5 100644
--- a/ultralytics/tracker/trackers/__init__.py
+++ b/ultralytics/tracker/trackers/__init__.py
@@ -2,3 +2,5 @@
from .bot_sort import BOTSORT
from .byte_tracker import BYTETracker
+
+__all__ = 'BOTSORT', 'BYTETracker' # allow simpler import
diff --git a/ultralytics/tracker/utils/gmc.py b/ultralytics/tracker/utils/gmc.py
index 653b5e8..09c4835 100644
--- a/ultralytics/tracker/utils/gmc.py
+++ b/ultralytics/tracker/utils/gmc.py
@@ -213,7 +213,7 @@ class GMC:
prev_pt = np.array(self.prevKeyPoints[m.queryIdx].pt, dtype=np.int_)
curr_pt = np.array(keypoints[m.trainIdx].pt, dtype=np.int_)
curr_pt[0] += W
- color = np.random.randint(0, 255, (3,))
+ color = np.random.randint(0, 255, 3)
color = (int(color[0]), int(color[1]), int(color[2]))
matches_img = cv2.line(matches_img, prev_pt, curr_pt, tuple(color), 1, cv2.LINE_AA)
diff --git a/ultralytics/yolo/__init__.py b/ultralytics/yolo/__init__.py
index 1eea113..13e457d 100644
--- a/ultralytics/yolo/__init__.py
+++ b/ultralytics/yolo/__init__.py
@@ -2,4 +2,4 @@
from . import v8
-__all__ = ['v8']
+__all__ = 'v8', # tuple or list
diff --git a/ultralytics/yolo/cfg/__init__.py b/ultralytics/yolo/cfg/__init__.py
index 2fb9895..9e8116d 100644
--- a/ultralytics/yolo/cfg/__init__.py
+++ b/ultralytics/yolo/cfg/__init__.py
@@ -269,6 +269,11 @@ def entrypoint(debug=''):
checks.check_yolo()
return
+ # Task
+ task = overrides.get('task')
+ if task and task not in TASKS:
+ raise ValueError(f"Invalid 'task={task}'. Valid tasks are {TASKS}.\n{CLI_HELP_MSG}")
+
# Model
model = overrides.pop('model', DEFAULT_CFG.model)
if model is None:
@@ -276,15 +281,11 @@ def entrypoint(debug=''):
LOGGER.warning(f"WARNING ⚠️ 'model' is missing. Using default 'model={model}'.")
from ultralytics.yolo.engine.model import YOLO
overrides['model'] = model
- model = YOLO(model)
+ model = YOLO(model, task=task)
- # Task
- task = overrides.get('task', model.task)
- if task is not None:
- if task not in TASKS:
- raise ValueError(f"Invalid 'task={task}'. Valid tasks are {TASKS}.\n{CLI_HELP_MSG}")
- else:
- model.task = task
+ # Task Update
+ task = task or model.task
+ overrides['task'] = task
# Mode
if mode in {'predict', 'track'} and 'source' not in overrides:
diff --git a/ultralytics/yolo/data/__init__.py b/ultralytics/yolo/data/__init__.py
index 08dbf74..a7bf7fa 100644
--- a/ultralytics/yolo/data/__init__.py
+++ b/ultralytics/yolo/data/__init__.py
@@ -5,12 +5,5 @@ from .build import build_classification_dataloader, build_dataloader, load_infer
from .dataset import ClassificationDataset, SemanticDataset, YOLODataset
from .dataset_wrappers import MixAndRectDataset
-__all__ = [
- 'BaseDataset',
- 'ClassificationDataset',
- 'MixAndRectDataset',
- 'SemanticDataset',
- 'YOLODataset',
- 'build_classification_dataloader',
- 'build_dataloader',
- 'load_inference_source',]
+__all__ = ('BaseDataset', 'ClassificationDataset', 'MixAndRectDataset', 'SemanticDataset', 'YOLODataset',
+ 'build_classification_dataloader', 'build_dataloader', 'load_inference_source')
diff --git a/ultralytics/yolo/data/augment.py b/ultralytics/yolo/data/augment.py
index 6f31799..136e015 100644
--- a/ultralytics/yolo/data/augment.py
+++ b/ultralytics/yolo/data/augment.py
@@ -564,7 +564,7 @@ class Albumentations:
A.CLAHE(p=0.01),
A.RandomBrightnessContrast(p=0.0),
A.RandomGamma(p=0.0),
- A.ImageCompression(quality_lower=75, p=0.0),] # transforms
+ A.ImageCompression(quality_lower=75, p=0.0)] # transforms
self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
@@ -671,14 +671,14 @@ def v8_transforms(dataset, imgsz, hyp):
shear=hyp.shear,
perspective=hyp.perspective,
pre_transform=LetterBox(new_shape=(imgsz, imgsz)),
- ),])
+ )])
return Compose([
pre_transform,
MixUp(dataset, pre_transform=pre_transform, p=hyp.mixup),
Albumentations(p=1.0),
RandomHSV(hgain=hyp.hsv_h, sgain=hyp.hsv_s, vgain=hyp.hsv_v),
RandomFlip(direction='vertical', p=hyp.flipud),
- RandomFlip(direction='horizontal', p=hyp.fliplr),]) # transforms
+ RandomFlip(direction='horizontal', p=hyp.fliplr)]) # transforms
# Classification augmentations -----------------------------------------------------------------------------------------
@@ -719,8 +719,8 @@ def classify_albumentations(
if vflip > 0:
T += [A.VerticalFlip(p=vflip)]
if jitter > 0:
- color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, saturation, 0 hue
- T += [A.ColorJitter(*color_jitter, 0)]
+ jitter = float(jitter)
+ T += [A.ColorJitter(jitter, jitter, jitter, 0)] # brightness, contrast, saturation, 0 hue
else: # Use fixed crop for eval set (reproducibility)
T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor
diff --git a/ultralytics/yolo/data/base.py b/ultralytics/yolo/data/base.py
index 7860b6d..7557751 100644
--- a/ultralytics/yolo/data/base.py
+++ b/ultralytics/yolo/data/base.py
@@ -24,20 +24,18 @@ class BaseDataset(Dataset):
label_path (str): label path, this can also be an ann_file or other custom label path.
"""
- def __init__(
- self,
- img_path,
- imgsz=640,
- cache=False,
- augment=True,
- hyp=None,
- prefix='',
- rect=False,
- batch_size=None,
- stride=32,
- pad=0.5,
- single_cls=False,
- ):
+ def __init__(self,
+ img_path,
+ imgsz=640,
+ cache=False,
+ augment=True,
+ hyp=None,
+ prefix='',
+ rect=False,
+ batch_size=None,
+ stride=32,
+ pad=0.5,
+ single_cls=False):
super().__init__()
self.img_path = img_path
self.imgsz = imgsz
diff --git a/ultralytics/yolo/data/dataloaders/v5augmentations.py b/ultralytics/yolo/data/dataloaders/v5augmentations.py
index 0595d7a..acd6d83 100644
--- a/ultralytics/yolo/data/dataloaders/v5augmentations.py
+++ b/ultralytics/yolo/data/dataloaders/v5augmentations.py
@@ -335,8 +335,8 @@ def classify_albumentations(
if vflip > 0:
T += [A.VerticalFlip(p=vflip)]
if jitter > 0:
- color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, satuaration, 0 hue
- T += [A.ColorJitter(*color_jitter, 0)]
+ jitter = float(jitter)
+ T += [A.ColorJitter(jitter, jitter, jitter, 0)] # brightness, contrast, satuaration, 0 hue
else: # Use fixed crop for eval set (reproducibility)
T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor
diff --git a/ultralytics/yolo/data/dataset.py b/ultralytics/yolo/data/dataset.py
index 76a3a32..abb743c 100644
--- a/ultralytics/yolo/data/dataset.py
+++ b/ultralytics/yolo/data/dataset.py
@@ -4,13 +4,16 @@ from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
+import cv2
+import numpy as np
+import torch
import torchvision
from tqdm import tqdm
from ..utils import NUM_THREADS, TQDM_BAR_FORMAT, is_dir_writeable
-from .augment import *
+from .augment import Compose, Format, Instances, LetterBox, classify_albumentations, classify_transforms, v8_transforms
from .base import BaseDataset
-from .utils import HELP_URL, LOCAL_RANK, get_hash, img2label_paths, verify_image_label
+from .utils import HELP_URL, LOCAL_RANK, LOGGER, get_hash, img2label_paths, verify_image_label
class YOLODataset(BaseDataset):
diff --git a/ultralytics/yolo/engine/exporter.py b/ultralytics/yolo/engine/exporter.py
index 886a0b1..dfdfa68 100644
--- a/ultralytics/yolo/engine/exporter.py
+++ b/ultralytics/yolo/engine/exporter.py
@@ -50,7 +50,6 @@ TensorFlow.js:
import json
import os
import platform
-import re
import subprocess
import time
import warnings
@@ -90,9 +89,9 @@ def export_formats():
['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
['TensorFlow GraphDef', 'pb', '.pb', True, True],
['TensorFlow Lite', 'tflite', '.tflite', True, False],
- ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
- ['TensorFlow.js', 'tfjs', '_web_model', False, False],
- ['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
+ ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', True, False],
+ ['TensorFlow.js', 'tfjs', '_web_model', True, False],
+ ['PaddlePaddle', 'paddle', '_paddle_model', True, True], ]
return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
@@ -100,6 +99,15 @@ EXPORT_FORMATS_LIST = list(export_formats()['Argument'][1:])
EXPORT_FORMATS_TABLE = str(export_formats())
+def gd_outputs(gd):
+ # TensorFlow GraphDef model output node names
+ name_list, input_list = [], []
+ for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef
+ name_list.append(node.name)
+ input_list.extend(node.input)
+ return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
+
+
def try_export(inner_func):
# YOLOv8 export decorator, i..e @try_export
inner_args = get_default_args(inner_func)
@@ -164,10 +172,10 @@ class Exporter:
# Checks
model.names = check_class_names(model.names)
self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2) # check image size
- if model.task == 'classify':
- self.args.nms = self.args.agnostic_nms = False
if self.args.optimize:
assert self.device.type == 'cpu', '--optimize 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/')
# Input
im = torch.zeros(self.args.batch, 3, *self.imgsz).to(self.device)
@@ -208,7 +216,7 @@ class Exporter:
self.file = file
self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else tuple(tuple(x.shape) for x in y)
self.pretty_name = self.file.stem.replace('yolo', 'YOLO')
- description = f'Ultralytics {self.pretty_name} model' + f'trained on {Path(self.args.data).name}' \
+ description = f'Ultralytics {self.pretty_name} model ' + f'trained on {Path(self.args.data).name}' \
if self.args.data else '(untrained)'
self.metadata = {
'description': description,
@@ -239,8 +247,7 @@ class Exporter:
'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(nms=nms or self.args.agnostic_nms or tfjs,
- agnostic_nms=self.args.agnostic_nms or tfjs)
+ 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:
@@ -386,7 +393,7 @@ class Exporter:
check_requirements('coremltools>=6.0')
import coremltools as ct # noqa
- class iOSModel(torch.nn.Module):
+ class iOSDetectModel(torch.nn.Module):
# Wrap an Ultralytics YOLO model for iOS export
def __init__(self, model, im):
super().__init__()
@@ -405,29 +412,36 @@ class Exporter:
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
f = self.file.with_suffix('.mlmodel')
+ bias = [0.0, 0.0, 0.0]
+ scale = 1 / 255
+ classifier_config = None
if self.model.task == 'classify':
bias = [-x for x in IMAGENET_MEAN]
scale = 1 / 255 / (sum(IMAGENET_STD) / 3)
classifier_config = ct.ClassifierConfig(list(self.model.names.values())) if self.args.nms else None
- else:
- bias = [0.0, 0.0, 0.0]
- scale = 1 / 255
- classifier_config = None
- model = iOSModel(self.model, self.im).eval() if self.args.nms else self.model
- ts = torch.jit.trace(model, self.im, strict=False) # TorchScript model
+ model = self.model
+ elif self.model.task == 'detect':
+ model = iOSDetectModel(self.model, self.im) if self.args.nms else self.model
+ elif self.model.task == 'segment':
+ # TODO CoreML Segmentation model pipelining
+ model = self.model
+
+ ts = torch.jit.trace(model.eval(), self.im, strict=False) # TorchScript model
ct_model = ct.convert(ts,
inputs=[ct.ImageType('image', shape=self.im.shape, scale=scale, bias=bias)],
classifier_config=classifier_config)
bits, mode = (8, 'kmeans_lut') if self.args.int8 else (16, 'linear') if self.args.half else (32, None)
if bits < 32:
ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
- if self.args.nms:
+ if self.args.nms and self.model.task == 'detect':
ct_model = self._pipeline_coreml(ct_model)
- ct_model.short_description = self.metadata['description']
- ct_model.author = self.metadata['author']
- ct_model.license = self.metadata['license']
- ct_model.version = self.metadata['version']
+ m = self.metadata # metadata dict
+ ct_model.short_description = m['description']
+ ct_model.author = m['author']
+ ct_model.license = m['license']
+ ct_model.version = m['version']
+ ct_model.user_defined_metadata.update({k: str(v) for k, v in m.items() if k in ('stride', 'task', 'names')})
ct_model.save(str(f))
return f, ct_model
@@ -497,14 +511,7 @@ class Exporter:
return f, None
@try_export
- def _export_saved_model(self,
- nms=False,
- agnostic_nms=False,
- topk_per_class=100,
- topk_all=100,
- iou_thres=0.45,
- conf_thres=0.25,
- prefix=colorstr('TensorFlow SavedModel:')):
+ def _export_saved_model(self, prefix=colorstr('TensorFlow SavedModel:')):
# YOLOv8 TensorFlow SavedModel export
try:
@@ -562,6 +569,9 @@ class Exporter:
@try_export
def _export_tflite(self, keras_model, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
# YOLOv8 TensorFlow Lite export
+ import tensorflow as tf # noqa
+
+ LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
saved_model = Path(str(self.file).replace(self.file.suffix, '_saved_model'))
if self.args.int8:
f = saved_model / (self.file.stem + 'yolov8n_integer_quant.tflite') # fp32 in/out
@@ -572,9 +582,6 @@ class Exporter:
return str(f), None # noqa
# OLD VERSION BELOW ---------------------------------------------------------------
- import tensorflow as tf # noqa
-
- LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
batch_size, ch, *imgsz = list(self.im.shape) # BCHW
f = str(self.file).replace(self.file.suffix, '-fp16.tflite')
@@ -619,7 +626,9 @@ class Exporter:
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 -',
+ # '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',
@@ -639,30 +648,36 @@ class Exporter:
def _export_tfjs(self, prefix=colorstr('TensorFlow.js:')):
# YOLOv8 TensorFlow.js export
check_requirements('tensorflowjs')
+ import tensorflow as tf
import tensorflowjs as tfjs # noqa
LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
f = str(self.file).replace(self.file.suffix, '_web_model') # js dir
f_pb = self.file.with_suffix('.pb') # *.pb path
- f_json = Path(f) / 'model.json' # *.json path
- cmd = f'tensorflowjs_converter --input_format=tf_frozen_model ' \
- f'--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}'
+ gd = tf.Graph().as_graph_def() # TF GraphDef
+ with open(f_pb, 'rb') as file:
+ gd.ParseFromString(file.read())
+ outputs = ','.join(gd_outputs(gd))
+ LOGGER.info(f'\n{prefix} output node names: {outputs}')
+
+ cmd = f'tensorflowjs_converter --input_format=tf_frozen_model --output_node_names={outputs} {f_pb} {f}'
subprocess.run(cmd.split(), check=True)
- with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order
- subst = re.sub(
- r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
- r'"Identity.?.?": {"name": "Identity.?.?"}, '
- r'"Identity.?.?": {"name": "Identity.?.?"}, '
- r'"Identity.?.?": {"name": "Identity.?.?"}}}',
- r'{"outputs": {"Identity": {"name": "Identity"}, '
- r'"Identity_1": {"name": "Identity_1"}, '
- r'"Identity_2": {"name": "Identity_2"}, '
- r'"Identity_3": {"name": "Identity_3"}}}',
- f_json.read_text(),
- )
- j.write(subst)
+ # f_json = Path(f) / 'model.json' # *.json path
+ # with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order
+ # subst = re.sub(
+ # r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
+ # r'"Identity.?.?": {"name": "Identity.?.?"}, '
+ # r'"Identity.?.?": {"name": "Identity.?.?"}, '
+ # r'"Identity.?.?": {"name": "Identity.?.?"}}}',
+ # r'{"outputs": {"Identity": {"name": "Identity"}, '
+ # r'"Identity_1": {"name": "Identity_1"}, '
+ # r'"Identity_2": {"name": "Identity_2"}, '
+ # r'"Identity_3": {"name": "Identity_3"}}}',
+ # f_json.read_text(),
+ # )
+ # j.write(subst)
yaml_save(Path(f) / 'metadata.yaml', self.metadata) # add metadata.yaml
return f, None
@@ -680,7 +695,7 @@ class Exporter:
model_meta.license = self.metadata['license']
# Label file
- tmp_file = file.parent / 'temp_meta.txt'
+ tmp_file = Path(file).parent / 'temp_meta.txt'
with open(tmp_file, 'w') as f:
f.write(str(self.metadata))
@@ -718,7 +733,7 @@ class Exporter:
b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
metadata_buf = b.Output()
- populator = _metadata.MetadataPopulator.with_model_file(file)
+ populator = _metadata.MetadataPopulator.with_model_file(str(file))
populator.load_metadata_buffer(metadata_buf)
populator.load_associated_files([str(tmp_file)])
populator.populate()
diff --git a/ultralytics/yolo/engine/model.py b/ultralytics/yolo/engine/model.py
index 2e65836..104dcaf 100644
--- a/ultralytics/yolo/engine/model.py
+++ b/ultralytics/yolo/engine/model.py
@@ -2,7 +2,6 @@
import sys
from pathlib import Path
-from typing import List
from ultralytics import yolo # noqa
from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight,
@@ -68,7 +67,7 @@ class YOLO:
list(ultralytics.yolo.engine.results.Results): The prediction results.
"""
- def __init__(self, model='yolov8n.pt') -> None:
+ def __init__(self, model='yolov8n.pt', task=None) -> None:
"""
Initializes the YOLO model.
@@ -91,9 +90,9 @@ class YOLO:
if not suffix and Path(model).stem in GITHUB_ASSET_STEMS:
model, suffix = Path(model).with_suffix('.pt'), '.pt' # add suffix, i.e. yolov8n -> yolov8n.pt
if suffix == '.yaml':
- self._new(model)
+ self._new(model, task)
else:
- self._load(model)
+ self._load(model, task)
def __call__(self, source=None, stream=False, **kwargs):
return self.predict(source, stream, **kwargs)
@@ -102,17 +101,18 @@ class YOLO:
name = self.__class__.__name__
raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
- def _new(self, cfg: str, verbose=True):
+ def _new(self, cfg: str, task=None, verbose=True):
"""
Initializes a new model and infers the task type from the model definitions.
Args:
cfg (str): model configuration file
+ task (str) or (None): model task
verbose (bool): display model info on load
"""
self.cfg = check_yaml(cfg) # check YAML
cfg_dict = yaml_load(self.cfg, append_filename=True) # model dict
- self.task = guess_model_task(cfg_dict)
+ self.task = task or guess_model_task(cfg_dict)
self.model = TASK_MAP[self.task][0](cfg_dict, verbose=verbose and RANK == -1) # build model
self.overrides['model'] = self.cfg
@@ -121,12 +121,13 @@ class YOLO:
self.model.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS} # attach args to model
self.model.task = self.task
- def _load(self, weights: str, task=''):
+ def _load(self, weights: str, task=None):
"""
Initializes a new model and infers the task type from the model head.
Args:
weights (str): model checkpoint to be loaded
+ task (str) or (None): model task
"""
suffix = Path(weights).suffix
if suffix == '.pt':
@@ -137,7 +138,7 @@ class YOLO:
else:
weights = check_file(weights)
self.model, self.ckpt = weights, None
- self.task = guess_model_task(weights)
+ self.task = task or guess_model_task(weights)
self.ckpt_path = weights
self.overrides['model'] = weights
diff --git a/ultralytics/yolo/engine/predictor.py b/ultralytics/yolo/engine/predictor.py
index 17eee83..de15d51 100644
--- a/ultralytics/yolo/engine/predictor.py
+++ b/ultralytics/yolo/engine/predictor.py
@@ -32,7 +32,6 @@ from collections import defaultdict
from pathlib import Path
import cv2
-import torch
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.cfg import get_cfg
diff --git a/ultralytics/yolo/engine/trainer.py b/ultralytics/yolo/engine/trainer.py
index 263750b..a0ce215 100644
--- a/ultralytics/yolo/engine/trainer.py
+++ b/ultralytics/yolo/engine/trainer.py
@@ -242,7 +242,7 @@ class BaseTrainer:
metric_keys = self.validator.metrics.keys + self.label_loss_items(prefix='val')
self.metrics = dict(zip(metric_keys, [0] * len(metric_keys))) # TODO: init metrics for plot_results()?
self.ema = ModelEMA(self.model)
- if self.args.plots:
+ if self.args.plots and not self.args.v5loader:
self.plot_training_labels()
self.resume_training(ckpt)
self.scheduler.last_epoch = self.start_epoch - 1 # do not move
diff --git a/ultralytics/yolo/utils/__init__.py b/ultralytics/yolo/utils/__init__.py
index e0ad000..da289d3 100644
--- a/ultralytics/yolo/utils/__init__.py
+++ b/ultralytics/yolo/utils/__init__.py
@@ -18,7 +18,6 @@ from typing import Union
import cv2
import numpy as np
import pandas as pd
-import requests
import torch
import yaml
@@ -517,10 +516,7 @@ def set_sentry():
((is_pip_package() and not is_git_dir()) or
(get_git_origin_url() == 'https://github.com/ultralytics/ultralytics.git' and get_git_branch() == 'main')):
- import hashlib
-
import sentry_sdk # noqa
-
sentry_sdk.init(
dsn='https://f805855f03bb4363bc1e16cb7d87b654@o4504521589325824.ingest.sentry.io/4504521592406016',
debug=False,
diff --git a/ultralytics/yolo/utils/benchmarks.py b/ultralytics/yolo/utils/benchmarks.py
index 993e6d1..3b3d1be 100644
--- a/ultralytics/yolo/utils/benchmarks.py
+++ b/ultralytics/yolo/utils/benchmarks.py
@@ -30,14 +30,14 @@ import pandas as pd
from ultralytics import YOLO
from ultralytics.yolo.engine.exporter import export_formats
-from ultralytics.yolo.utils import LOGGER, ROOT, SETTINGS
+from ultralytics.yolo.utils import LINUX, LOGGER, ROOT, SETTINGS
from ultralytics.yolo.utils.checks import check_yolo
from ultralytics.yolo.utils.downloads import download
from ultralytics.yolo.utils.files import file_size
from ultralytics.yolo.utils.torch_utils import select_device
-def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, half=False, device='cpu', hard_fail=0.30):
+def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, half=False, device='cpu', hard_fail=False):
device = select_device(device, verbose=False)
if isinstance(model, (str, Path)):
model = YOLO(model)
@@ -45,11 +45,10 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, hal
y = []
t0 = time.time()
for i, (name, format, suffix, cpu, gpu) in export_formats().iterrows(): # index, (name, format, suffix, CPU, GPU)
+ emoji = '❌' # indicates export failure
try:
- assert i not in (9, 10), 'inference not supported' # Edge TPU and TF.js are unsupported
- assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13' # CoreML
- assert i != 11 or model.task != 'classify', 'paddle-classify bug'
-
+ assert i != 11, 'paddle exports coming soon'
+ assert i != 9 or LINUX, 'Edge TPU export only supported on Linux'
if 'cpu' in device.type:
assert cpu, 'inference not supported on CPU'
if 'cuda' in device.type:
@@ -61,13 +60,16 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, hal
export = model # PyTorch format
else:
filename = model.export(imgsz=imgsz, format=format, half=half, device=device) # all others
- export = YOLO(filename)
+ export = YOLO(filename, task=model.task)
assert suffix in str(filename), 'export failed'
+ emoji = '❎' # indicates export succeeded
# Predict
+ assert i not in (9, 10), 'inference not supported' # Edge TPU and TF.js are unsupported
+ assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13' # CoreML
if not (ROOT / 'assets/bus.jpg').exists():
download(url='https://ultralytics.com/images/bus.jpg', dir=ROOT / 'assets')
- export.predict(ROOT / 'assets/bus.jpg', imgsz=imgsz, device=device, half=half) # test
+ export.predict(ROOT / 'assets/bus.jpg', imgsz=imgsz, device=device, half=half)
# Validate
if model.task == 'detect':
@@ -84,17 +86,16 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, hal
if hard_fail:
assert type(e) is AssertionError, f'Benchmark hard_fail for {name}: {e}'
LOGGER.warning(f'ERROR ❌️ Benchmark failure for {name}: {e}')
- y.append([name, '❌', None, None, None]) # mAP, t_inference
+ y.append([name, emoji, None, None, None]) # mAP, t_inference
# Print results
check_yolo(device=device) # print system info
- c = ['Format', 'Status❔', 'Size (MB)', key, 'Inference time (ms/im)']
- df = pd.DataFrame(y, columns=c)
+ df = pd.DataFrame(y, columns=['Format', 'Status❔', 'Size (MB)', key, 'Inference time (ms/im)'])
name = Path(model.ckpt_path).name
s = f'\nBenchmarks complete for {name} on {data} at imgsz={imgsz} ({time.time() - t0:.2f}s)\n{df}\n'
LOGGER.info(s)
- with open('benchmarks.log', 'a') as f:
+ with open('benchmarks.log', 'a', errors='ignore', encoding='utf-8') as f:
f.write(s)
if hard_fail and isinstance(hard_fail, float):
diff --git a/ultralytics/yolo/utils/callbacks/__init__.py b/ultralytics/yolo/utils/callbacks/__init__.py
index a885dec..fb5dfe2 100644
--- a/ultralytics/yolo/utils/callbacks/__init__.py
+++ b/ultralytics/yolo/utils/callbacks/__init__.py
@@ -1,5 +1,3 @@
from .base import add_integration_callbacks, default_callbacks
-__all__ = [
- 'add_integration_callbacks',
- 'default_callbacks',]
+__all__ = 'add_integration_callbacks', 'default_callbacks'
diff --git a/ultralytics/yolo/utils/checks.py b/ultralytics/yolo/utils/checks.py
index e40f787..013f5ad 100644
--- a/ultralytics/yolo/utils/checks.py
+++ b/ultralytics/yolo/utils/checks.py
@@ -137,7 +137,6 @@ def check_latest_pypi_version(package_name='ultralytics'):
def check_pip_update():
from ultralytics import __version__
latest = check_latest_pypi_version()
- latest = '9.0.0'
if pkg.parse_version(__version__) < pkg.parse_version(latest):
LOGGER.info(f'New https://pypi.org/project/ultralytics/{latest} available 😃 '
f"Update with 'pip install -U ultralytics'")
@@ -239,7 +238,7 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
LOGGER.warning(f'{prefix} ❌ {e}')
-def check_suffix(file='yolov8n.pt', suffix=('.pt',), msg=''):
+def check_suffix(file='yolov8n.pt', suffix='.pt', msg=''):
# Check file(s) for acceptable suffix
if file and suffix:
if isinstance(suffix, str):
diff --git a/ultralytics/yolo/utils/instance.py b/ultralytics/yolo/utils/instance.py
index cee4a43..95a62ca 100644
--- a/ultralytics/yolo/utils/instance.py
+++ b/ultralytics/yolo/utils/instance.py
@@ -10,9 +10,8 @@ import numpy as np
from .ops import ltwh2xywh, ltwh2xyxy, resample_segments, xywh2ltwh, xywh2xyxy, xyxy2ltwh, xyxy2xywh
-# From PyTorch internals
def _ntuple(n):
-
+ # From PyTorch internals
def parse(x):
return x if isinstance(x, abc.Iterable) else tuple(repeat(x, n))
@@ -26,7 +25,7 @@ to_4tuple = _ntuple(4)
# `ltwh` means left top and width, height(coco format)
_formats = ['xyxy', 'xywh', 'ltwh']
-__all__ = ['Bboxes']
+__all__ = 'Bboxes', # tuple or list
class Bboxes:
diff --git a/ultralytics/yolo/utils/plotting.py b/ultralytics/yolo/utils/plotting.py
index 76c042c..fd773b4 100644
--- a/ultralytics/yolo/utils/plotting.py
+++ b/ultralytics/yolo/utils/plotting.py
@@ -207,8 +207,7 @@ def plot_labels(boxes, cls, names=(), save_dir=Path('')):
def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):
# Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
- xyxy = torch.Tensor(xyxy).view(-1, 4)
- b = xyxy2xywh(xyxy) # boxes
+ b = xyxy2xywh(xyxy.view(-1, 4)) # boxes
if square:
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
diff --git a/ultralytics/yolo/utils/torch_utils.py b/ultralytics/yolo/utils/torch_utils.py
index 84cb5ec..e918808 100644
--- a/ultralytics/yolo/utils/torch_utils.py
+++ b/ultralytics/yolo/utils/torch_utils.py
@@ -195,7 +195,7 @@ def get_flops(model, imgsz=640):
p = next(model.parameters())
stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # max stride
im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
- flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2 # stride GFLOPs
+ flops = thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1E9 * 2 # stride GFLOPs
imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
flops = flops * imgsz[0] / stride * imgsz[1] / stride # 640x640 GFLOPs
return flops
@@ -374,7 +374,7 @@ def profile(input, ops, n=10, device=None):
m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward
try:
- flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs
+ flops = thop.profile(m, inputs=[x], verbose=False)[0] / 1E9 * 2 # GFLOPs
except Exception:
flops = 0
diff --git a/ultralytics/yolo/v8/__init__.py b/ultralytics/yolo/v8/__init__.py
index 50c710d..1f03762 100644
--- a/ultralytics/yolo/v8/__init__.py
+++ b/ultralytics/yolo/v8/__init__.py
@@ -2,4 +2,4 @@
from ultralytics.yolo.v8 import classify, detect, segment
-__all__ = ['classify', 'segment', 'detect']
+__all__ = 'classify', 'segment', 'detect'
diff --git a/ultralytics/yolo/v8/classify/__init__.py b/ultralytics/yolo/v8/classify/__init__.py
index 6b2d641..7fff3a8 100644
--- a/ultralytics/yolo/v8/classify/__init__.py
+++ b/ultralytics/yolo/v8/classify/__init__.py
@@ -4,4 +4,4 @@ from ultralytics.yolo.v8.classify.predict import ClassificationPredictor, predic
from ultralytics.yolo.v8.classify.train import ClassificationTrainer, train
from ultralytics.yolo.v8.classify.val import ClassificationValidator, val
-__all__ = ['ClassificationPredictor', 'predict', 'ClassificationTrainer', 'train', 'ClassificationValidator', 'val']
+__all__ = 'ClassificationPredictor', 'predict', 'ClassificationTrainer', 'train', 'ClassificationValidator', 'val'
diff --git a/ultralytics/yolo/v8/detect/__init__.py b/ultralytics/yolo/v8/detect/__init__.py
index 2145179..972b5e8 100644
--- a/ultralytics/yolo/v8/detect/__init__.py
+++ b/ultralytics/yolo/v8/detect/__init__.py
@@ -4,4 +4,4 @@ from .predict import DetectionPredictor, predict
from .train import DetectionTrainer, train
from .val import DetectionValidator, val
-__all__ = ['DetectionPredictor', 'predict', 'DetectionTrainer', 'train', 'DetectionValidator', 'val']
+__all__ = 'DetectionPredictor', 'predict', 'DetectionTrainer', 'train', 'DetectionValidator', 'val'
diff --git a/ultralytics/yolo/v8/segment/__init__.py b/ultralytics/yolo/v8/segment/__init__.py
index 099c8a5..a9831ac 100644
--- a/ultralytics/yolo/v8/segment/__init__.py
+++ b/ultralytics/yolo/v8/segment/__init__.py
@@ -4,4 +4,4 @@ from .predict import SegmentationPredictor, predict
from .train import SegmentationTrainer, train
from .val import SegmentationValidator, val
-__all__ = ['SegmentationPredictor', 'predict', 'SegmentationTrainer', 'train', 'SegmentationValidator', 'val']
+__all__ = 'SegmentationPredictor', 'predict', 'SegmentationTrainer', 'train', 'SegmentationValidator', 'val'