ultralytics 8.0.37 add TFLite metadata in AutoBackend (#953)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Aarni Koskela <akx@iki.fi>

.gitignore

@@ -81,6 +81,9 @@ target/
 profile_default/
 ipython_config.py
 
+# Profiling
+*.pclprof
+
 # pyenv
 .python-version
 

README.md

@@ -216,9 +216,7 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) fo
 
 ## <div align="center">Ultralytics HUB</div>
 
-[Ultralytics HUB](https://bit.ly/ultralytics_hub) is our ⭐ **NEW** no-code solution to visualize datasets, train YOLOv8
+Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 (coming soon) 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now!
-🚀 models, and deploy to the real world in a seamless experience. Get started for **Free** now! Also run YOLOv8 models on
-your iOS or Android device by downloading the [Ultralytics App](https://ultralytics.com/app_install)!
 
 <a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>

requirements.txt

@@ -34,10 +34,10 @@ seaborn>=0.11.0
 # openvino-dev>=2022.3  # OpenVINO export
 
 # Extras --------------------------------------
-ipython  # interactive notebook
 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
 # roboflow

ultralytics/__init__.py

@@ -1,8 +1,8 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = "8.0.36"
+__version__ = "8.0.37"
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils.checks import check_yolo as checks
 
-__all__ = ["__version__", "YOLO", "hub", "checks"]  # allow simpler import
+__all__ = ["__version__", "YOLO", "checks"]  # allow simpler import

ultralytics/hub/utils.py

@@ -12,7 +12,7 @@ from random import random
 import requests
 
 from ultralytics.yolo.utils import (DEFAULT_CFG_DICT, ENVIRONMENT, LOGGER, RANK, SETTINGS, TryExcept, __version__,
-                                    colorstr, emojis, get_git_origin_url, is_git_dir, is_github_actions_ci,
+                                    colorstr, emojis, get_git_origin_url, is_colab, is_git_dir, is_github_actions_ci,
                                     is_pip_package, is_pytest_running)
 from ultralytics.yolo.utils.checks import check_online
 
@@ -36,6 +36,8 @@ def check_dataset_disk_space(url='https://ultralytics.com/assets/coco128.zip', s
 
 def request_with_credentials(url: str) -> any:
     """ Make an ajax request with cookies attached """
+    if not is_colab():
+        raise OSError('request_with_credentials() must run in a Colab environment')
     from google.colab import output  # noqa
     from IPython import display  # noqa
     display.display(

ultralytics/nn/autobackend.py

@@ -1,7 +1,9 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
-
+import ast
+import contextlib
 import json
 import platform
+import zipfile
 from collections import OrderedDict, namedtuple
 from pathlib import Path
 from urllib.parse import urlparse
@@ -207,6 +209,12 @@ class AutoBackend(nn.Module):
             interpreter.allocate_tensors()  # allocate
             input_details = interpreter.get_input_details()  # inputs
             output_details = interpreter.get_output_details()  # outputs
+            # load metadata
+            with contextlib.suppress(zipfile.BadZipFile):
+                with zipfile.ZipFile(w, "r") as model:
+                    meta_file = model.namelist()[0]
+                    meta = ast.literal_eval(model.read(meta_file).decode("utf-8"))
+                    stride, names = int(meta['stride']), meta['names']
         elif tfjs:  # TF.js
             raise NotImplementedError('ERROR: YOLOv8 TF.js inference is not supported')
         elif paddle:  # PaddlePaddle
@@ -214,7 +222,7 @@ class AutoBackend(nn.Module):
             check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
             import paddle.inference as pdi
             if not Path(w).is_file():  # if not *.pdmodel
-                w = next(Path(w).rglob('*.pdmodel'))  # get *.xml file from *_openvino_model dir
+                w = next(Path(w).rglob('*.pdmodel'))  # get *.pdmodel file from *_paddle_model dir
             weights = Path(w).with_suffix('.pdiparams')
             config = pdi.Config(str(w), str(weights))
             if cuda:
@@ -328,6 +336,9 @@ class AutoBackend(nn.Module):
                         scale, zero_point = output['quantization']
                         x = (x.astype(np.float32) - zero_point) * scale  # re-scale
                     y.append(x)
+                # TF segment fixes: export is reversed vs ONNX export and protos are transposed
+                if len(self.output_details) == 2:  # segment
+                    y = [y[1], np.transpose(y[0], (0, 3, 1, 2))]
             y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
             y[0][..., :4] *= [w, h, w, h]  # xywh normalized to pixels
 

ultralytics/nn/tasks.py

@@ -1,5 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
+import ast
 import contextlib
 from copy import deepcopy
 from pathlib import Path
@@ -427,6 +428,8 @@ def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
     for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
         m = eval(m) if isinstance(m, str) else m  # eval strings
         for j, a in enumerate(args):
+            # TODO: re-implement with eval() removal if possible
+            # args[j] = (locals()[a] if a in locals() else ast.literal_eval(a)) if isinstance(a, str) else a
             with contextlib.suppress(NameError):
                 args[j] = eval(a) if isinstance(a, str) else a  # eval strings
 
@@ -480,28 +483,9 @@ def guess_model_task(model):
     Raises:
         SyntaxError: If the task of the model could not be determined.
     """
-    cfg = None
-    if isinstance(model, dict):
-        cfg = model
-    elif isinstance(model, nn.Module):  # PyTorch model
-        for x in 'model.args', 'model.model.args', 'model.model.model.args':
-            with contextlib.suppress(Exception):
-                return eval(x)['task']
-        for x in 'model.yaml', 'model.model.yaml', 'model.model.model.yaml':
-            with contextlib.suppress(Exception):
-                cfg = eval(x)
-                break
-    elif isinstance(model, (str, Path)):
-        model = str(model)
-        if '-seg' in model:
-            return "segment"
-        elif '-cls' in model:
-            return "classify"
-        else:
-            return "detect"
 
-    # Guess from YAML dictionary
+    def cfg2task(cfg):
-    if cfg:
+        # Guess from YAML dictionary
         m = cfg["head"][-1][-2].lower()  # output module name
         if m in ["classify", "classifier", "cls", "fc"]:
             return "classify"
@@ -510,8 +494,20 @@ def guess_model_task(model):
         if m in ["segment"]:
             return "segment"
 
+    # Guess from model cfg
+    if isinstance(model, dict):
+        with contextlib.suppress(Exception):
+            return cfg2task(model)
+
     # Guess from PyTorch model
-    if isinstance(model, nn.Module):
+    if isinstance(model, nn.Module):  # PyTorch model
+        for x in 'model.args', 'model.model.args', 'model.model.model.args':
+            with contextlib.suppress(Exception):
+                return eval(x)['task']
+        for x in 'model.yaml', 'model.model.yaml', 'model.model.model.yaml':
+            with contextlib.suppress(Exception):
+                return cfg2task(eval(x))
+
         for m in model.modules():
             if isinstance(m, Detect):
                 return "detect"
@@ -520,6 +516,16 @@ def guess_model_task(model):
             elif isinstance(m, Classify):
                 return "classify"
 
+    # Guess from model filename
+    if isinstance(model, (str, Path)):
+        model = Path(model).stem
+        if '-seg' in model:
+            return "segment"
+        elif '-cls' in model:
+            return "classify"
+        else:
+            return "detect"
+
     # Unable to determine task from model
     raise SyntaxError("YOLO is unable to automatically guess model task. Explicitly define task for your model, "
                       "i.e. 'task=detect', 'task=segment' or 'task=classify'.")

ultralytics/yolo/__init__.py

@@ -1,3 +1,5 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
 from . import v8
+
+__all__ = ["v8"]

ultralytics/yolo/cfg/__init__.py

@@ -142,7 +142,7 @@ def check_cfg_mismatch(base: Dict, custom: Dict, e=None):
         string = ''
         for x in mismatched:
             matches = get_close_matches(x, base)  # key list
-            matches = [f"{k}={DEFAULT_CFG_DICT[k]}" if DEFAULT_CFG_DICT[k] is not None else k for k in matches]  # k=v
+            matches = [f"{k}={DEFAULT_CFG_DICT[k]}" if DEFAULT_CFG_DICT.get(k) is not None else k for k in matches]
             match_str = f"Similar arguments are i.e. {matches}." if matches else ''
             string += f"'{colorstr('red', 'bold', x)}' is not a valid YOLO argument. {match_str}\n"
         raise SyntaxError(string + CLI_HELP_MSG) from e

ultralytics/yolo/data/__init__.py

@@ -4,3 +4,13 @@ from .base import BaseDataset
 from .build import build_classification_dataloader, build_dataloader, load_inference_source
 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",]

ultralytics/yolo/engine/exporter.py

@@ -73,7 +73,7 @@ from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, __version__, callbacks,
 from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
 from ultralytics.yolo.utils.files import file_size
 from ultralytics.yolo.utils.ops import Profile
-from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode, get_latest_opset
+from ultralytics.yolo.utils.torch_utils import get_latest_opset, select_device, smart_inference_mode
 
 MACOS = platform.system() == 'Darwin'  # macOS environment
 
@@ -508,7 +508,7 @@ class Exporter:
         onnx = self.file.with_suffix('.onnx')
 
         # Export to TF SavedModel
-        subprocess.run(f'onnx2tf -i {onnx} --output_signaturedefs -o {f}', shell=True)
+        subprocess.run(f'onnx2tf -i {onnx} -o {f} --non_verbose', shell=True)
 
         # Add TFLite metadata
         for tflite_file in Path(f).rglob('*.tflite'):

ultralytics/yolo/engine/model.py

@@ -108,8 +108,8 @@ class YOLO:
         Raises TypeError is model is not a PyTorch model
         """
         if not isinstance(self.model, nn.Module):
-            raise TypeError(f"model='{self.model}' must be a PyTorch model, but is a different type. PyTorch models "
+            raise TypeError(f"model='{self.model}' must be a *.pt PyTorch model, but is a different type. "
-                            f"can be used to train, val, predict and export, i.e. "
+                            f"PyTorch models can be used to train, val, predict and export, i.e. "
                             f"'yolo export model=yolov8n.pt', but exported formats like ONNX, TensorRT etc. only "
                             f"support 'predict' and 'val' modes, i.e. 'yolo predict model=yolov8n.onnx'.")
 
@@ -240,7 +240,7 @@ class YOLO:
         if RANK in {0, -1}:
             self.model, _ = attempt_load_one_weight(str(self.trainer.best))
             self.overrides = self.model.args
-        self.metrics_data = self.trainer.validator.metrics
+            self.metrics_data = self.trainer.validator.metrics
 
     def to(self, device):
         """

ultralytics/yolo/utils/__init__.py

@@ -221,11 +221,10 @@ def is_jupyter():
     Returns:
         bool: True if running inside a Jupyter Notebook, False otherwise.
     """
-    try:
+    with contextlib.suppress(Exception):
         from IPython import get_ipython
         return get_ipython() is not None
-    except ImportError:
+    return False
-        return False
 
 
 def is_docker() -> bool:
@@ -287,11 +286,9 @@ def is_pytest_running():
     Returns:
         (bool): True if pytest is running, False otherwise.
     """
-    try:
+    with contextlib.suppress(Exception):
-        import sys
         return "pytest" in sys.modules
-    except ImportError:
+    return False
-        return False
 
 
 def is_github_actions_ci() -> bool:

ultralytics/yolo/utils/callbacks/__init__.py

@@ -1 +1,5 @@
 from .base import add_integration_callbacks, default_callbacks
+
+__all__ = [
+    'add_integration_callbacks',
+    'default_callbacks',]

ultralytics/yolo/utils/checks.py

@@ -17,7 +17,6 @@ import numpy as np
 import pkg_resources as pkg
 import psutil
 import torch
-from IPython import display
 from matplotlib import font_manager
 
 from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads, emojis,
@@ -292,8 +291,10 @@ def check_yolo(verbose=True):
         gib = 1 << 30  # bytes per GiB
         ram = psutil.virtual_memory().total
         total, used, free = shutil.disk_usage("/")
-        display.clear_output()
         s = f'({os.cpu_count()} CPUs, {ram / gib:.1f} GB RAM, {(total - free) / gib:.1f}/{total / gib:.1f} GB disk)'
+        with contextlib.suppress(Exception):  # clear display if ipython is installed
+            from IPython import display
+            display.clear_output()
     else:
         s = ''
 

ultralytics/yolo/v8/classify/__init__.py

@@ -3,3 +3,5 @@
 from ultralytics.yolo.v8.classify.predict import ClassificationPredictor, predict
 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"]

ultralytics/yolo/v8/detect/__init__.py

@@ -3,3 +3,5 @@
 from .predict import DetectionPredictor, predict
 from .train import DetectionTrainer, train
 from .val import DetectionValidator, val
+
+__all__ = ["DetectionPredictor", "predict", "DetectionTrainer", "train", "DetectionValidator", "val"]

ultralytics/yolo/v8/segment/__init__.py

@@ -3,3 +3,5 @@
 from .predict import SegmentationPredictor, predict
 from .train import SegmentationTrainer, train
 from .val import SegmentationValidator, val
+
+__all__ = ["SegmentationPredictor", "predict", "SegmentationTrainer", "train", "SegmentationValidator", "val"]