fixed tensor context is none issue

README.md

@@ -38,7 +38,7 @@ Intended for educational use 🎓, our aim is to highlight the vulnerability of
    - 🛑 Is it a `pip is not recognized...` error? [WATCH THIS!](https://youtu.be/zWYvRS7DtOg)
 3. Fire up `PowerShell` or `Command Prompt` on Windows 🔍.
 4. To install `PyTorch`, select the appropriate command based on your GPU.
-    - Nvidia `pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118`
+    - Nvidia `pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118`
     - AMD or CPU `pip install torch torchvision torchaudio`
 5. 📦 Run the command below to install the required Open Source packages:
 ```

export.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
 
@@ -77,6 +77,25 @@ from utils.torch_utils import select_device, smart_inference_mode
 MACOS = platform.system() == 'Darwin'  # macOS environment
 
 
+class iOSModel(torch.nn.Module):
+
+    def __init__(self, model, im):
+        super().__init__()
+        b, c, h, w = im.shape  # batch, channel, height, width
+        self.model = model
+        self.nc = model.nc  # number of classes
+        if w == h:
+            self.normalize = 1. / w
+        else:
+            self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h])  # broadcast (slower, smaller)
+            # np = model(im)[0].shape[1]  # number of points
+            # self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h]).expand(np, 4)  # explicit (faster, larger)
+
+    def forward(self, x):
+        xywh, conf, cls = self.model(x)[0].squeeze().split((4, 1, self.nc), 1)
+        return cls * conf, xywh * self.normalize  # confidence (3780, 80), coordinates (3780, 4)
+
+
 def export_formats():
     # YOLOv5 export formats
     x = [
@@ -136,7 +155,7 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX
     import onnx
 
     LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
-    f = file.with_suffix('.onnx')
+    f = str(file.with_suffix('.onnx'))
 
     output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0']
     if dynamic:
@@ -186,23 +205,68 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX
 
 
 @try_export
-def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')):
+def export_openvino(file, metadata, half, int8, data, prefix=colorstr('OpenVINO:')):
     # YOLOv5 OpenVINO export
-    check_requirements('openvino-dev')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+    check_requirements('openvino-dev>=2023.0')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
-    import openvino.inference_engine as ie
+    import openvino.runtime as ov  # noqa
+    from openvino.tools import mo  # noqa
 
-    LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
+    LOGGER.info(f'\n{prefix} starting export with openvino {ov.__version__}...')
-    f = str(file).replace('.pt', f'_openvino_model{os.sep}')
+    f = str(file).replace(file.suffix, f'_openvino_model{os.sep}')
+    f_onnx = file.with_suffix('.onnx')
+    f_ov = str(Path(f) / file.with_suffix('.xml').name)
+    if int8:
+        check_requirements('nncf>=2.4.0')  # requires at least version 2.4.0 to use the post-training quantization
+        import nncf
+        import numpy as np
+        from openvino.runtime import Core
 
-    args = [
+        from utils.dataloaders import create_dataloader
-        'mo',
+        core = Core()
-        '--input_model',
+        onnx_model = core.read_model(f_onnx)  # export
-        str(file.with_suffix('.onnx')),
+
-        '--output_dir',
+        def prepare_input_tensor(image: np.ndarray):
-        f,
+            input_tensor = image.astype(np.float32)  # uint8 to fp16/32
-        '--data_type',
+            input_tensor /= 255.0  # 0 - 255 to 0.0 - 1.0
-        ('FP16' if half else 'FP32'),]
+
-    subprocess.run(args, check=True, env=os.environ)  # export
+            if input_tensor.ndim == 3:
+                input_tensor = np.expand_dims(input_tensor, 0)
+            return input_tensor
+
+        def gen_dataloader(yaml_path, task='train', imgsz=640, workers=4):
+            data_yaml = check_yaml(yaml_path)
+            data = check_dataset(data_yaml)
+            dataloader = create_dataloader(data[task],
+                                           imgsz=imgsz,
+                                           batch_size=1,
+                                           stride=32,
+                                           pad=0.5,
+                                           single_cls=False,
+                                           rect=False,
+                                           workers=workers)[0]
+            return dataloader
+
+        # noqa: F811
+
+        def transform_fn(data_item):
+            """
+            Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
+            Parameters:
+               data_item: Tuple with data item produced by DataLoader during iteration
+            Returns:
+                input_tensor: Input data for quantization
+            """
+            img = data_item[0].numpy()
+            input_tensor = prepare_input_tensor(img)
+            return input_tensor
+
+        ds = gen_dataloader(data)
+        quantization_dataset = nncf.Dataset(ds, transform_fn)
+        ov_model = nncf.quantize(onnx_model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED)
+    else:
+        ov_model = mo.convert_model(f_onnx, model_name=file.stem, framework='onnx', compress_to_fp16=half)  # export
+
+    ov.serialize(ov_model, f_ov)  # save
     yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata)  # add metadata.yaml
     return f, None
 
@@ -223,7 +287,7 @@ def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePaddle:')):
 
 
 @try_export
-def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
+def export_coreml(model, im, file, int8, half, nms, prefix=colorstr('CoreML:')):
     # YOLOv5 CoreML export
     check_requirements('coremltools')
     import coremltools as ct
@@ -231,6 +295,8 @@ def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
     LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
     f = file.with_suffix('.mlmodel')
 
+    if nms:
+        model = iOSModel(model, im)
     ts = torch.jit.trace(model, im, strict=False)  # TorchScript model
     ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
     bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None)
@@ -506,6 +572,129 @@ def add_tflite_metadata(file, metadata, num_outputs):
         tmp_file.unlink()
 
 
+def pipeline_coreml(model, im, file, names, y, prefix=colorstr('CoreML Pipeline:')):
+    # YOLOv5 CoreML pipeline
+    import coremltools as ct
+    from PIL import Image
+
+    print(f'{prefix} starting pipeline with coremltools {ct.__version__}...')
+    batch_size, ch, h, w = list(im.shape)  # BCHW
+    t = time.time()
+
+    # YOLOv5 Output shapes
+    spec = model.get_spec()
+    out0, out1 = iter(spec.description.output)
+    if platform.system() == 'Darwin':
+        img = Image.new('RGB', (w, h))  # img(192 width, 320 height)
+        # img = torch.zeros((*opt.img_size, 3)).numpy()  # img size(320,192,3) iDetection
+        out = model.predict({'image': img})
+        out0_shape, out1_shape = out[out0.name].shape, out[out1.name].shape
+    else:  # linux and windows can not run model.predict(), get sizes from pytorch output y
+        s = tuple(y[0].shape)
+        out0_shape, out1_shape = (s[1], s[2] - 5), (s[1], 4)  # (3780, 80), (3780, 4)
+
+    # Checks
+    nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height
+    na, nc = out0_shape
+    # na, nc = out0.type.multiArrayType.shape  # number anchors, classes
+    assert len(names) == nc, f'{len(names)} names found for nc={nc}'  # check
+
+    # Define output shapes (missing)
+    out0.type.multiArrayType.shape[:] = out0_shape  # (3780, 80)
+    out1.type.multiArrayType.shape[:] = out1_shape  # (3780, 4)
+    # spec.neuralNetwork.preprocessing[0].featureName = '0'
+
+    # Flexible input shapes
+    # from coremltools.models.neural_network import flexible_shape_utils
+    # s = [] # shapes
+    # s.append(flexible_shape_utils.NeuralNetworkImageSize(320, 192))
+    # s.append(flexible_shape_utils.NeuralNetworkImageSize(640, 384))  # (height, width)
+    # flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='image', sizes=s)
+    # r = flexible_shape_utils.NeuralNetworkImageSizeRange()  # shape ranges
+    # r.add_height_range((192, 640))
+    # r.add_width_range((192, 640))
+    # flexible_shape_utils.update_image_size_range(spec, feature_name='image', size_range=r)
+
+    # Print
+    print(spec.description)
+
+    # Model from spec
+    model = ct.models.MLModel(spec)
+
+    # 3. Create NMS protobuf
+    nms_spec = ct.proto.Model_pb2.Model()
+    nms_spec.specificationVersion = 5
+    for i in range(2):
+        decoder_output = model._spec.description.output[i].SerializeToString()
+        nms_spec.description.input.add()
+        nms_spec.description.input[i].ParseFromString(decoder_output)
+        nms_spec.description.output.add()
+        nms_spec.description.output[i].ParseFromString(decoder_output)
+
+    nms_spec.description.output[0].name = 'confidence'
+    nms_spec.description.output[1].name = 'coordinates'
+
+    output_sizes = [nc, 4]
+    for i in range(2):
+        ma_type = nms_spec.description.output[i].type.multiArrayType
+        ma_type.shapeRange.sizeRanges.add()
+        ma_type.shapeRange.sizeRanges[0].lowerBound = 0
+        ma_type.shapeRange.sizeRanges[0].upperBound = -1
+        ma_type.shapeRange.sizeRanges.add()
+        ma_type.shapeRange.sizeRanges[1].lowerBound = output_sizes[i]
+        ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i]
+        del ma_type.shape[:]
+
+    nms = nms_spec.nonMaximumSuppression
+    nms.confidenceInputFeatureName = out0.name  # 1x507x80
+    nms.coordinatesInputFeatureName = out1.name  # 1x507x4
+    nms.confidenceOutputFeatureName = 'confidence'
+    nms.coordinatesOutputFeatureName = 'coordinates'
+    nms.iouThresholdInputFeatureName = 'iouThreshold'
+    nms.confidenceThresholdInputFeatureName = 'confidenceThreshold'
+    nms.iouThreshold = 0.45
+    nms.confidenceThreshold = 0.25
+    nms.pickTop.perClass = True
+    nms.stringClassLabels.vector.extend(names.values())
+    nms_model = ct.models.MLModel(nms_spec)
+
+    # 4. Pipeline models together
+    pipeline = ct.models.pipeline.Pipeline(input_features=[('image', ct.models.datatypes.Array(3, ny, nx)),
+                                                           ('iouThreshold', ct.models.datatypes.Double()),
+                                                           ('confidenceThreshold', ct.models.datatypes.Double())],
+                                           output_features=['confidence', 'coordinates'])
+    pipeline.add_model(model)
+    pipeline.add_model(nms_model)
+
+    # Correct datatypes
+    pipeline.spec.description.input[0].ParseFromString(model._spec.description.input[0].SerializeToString())
+    pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString())
+    pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString())
+
+    # Update metadata
+    pipeline.spec.specificationVersion = 5
+    pipeline.spec.description.metadata.versionString = 'https://github.com/ultralytics/yolov5'
+    pipeline.spec.description.metadata.shortDescription = 'https://github.com/ultralytics/yolov5'
+    pipeline.spec.description.metadata.author = 'glenn.jocher@ultralytics.com'
+    pipeline.spec.description.metadata.license = 'https://github.com/ultralytics/yolov5/blob/master/LICENSE'
+    pipeline.spec.description.metadata.userDefined.update({
+        'classes': ','.join(names.values()),
+        'iou_threshold': str(nms.iouThreshold),
+        'confidence_threshold': str(nms.confidenceThreshold)})
+
+    # Save the model
+    f = file.with_suffix('.mlmodel')  # filename
+    model = ct.models.MLModel(pipeline.spec)
+    model.input_description['image'] = 'Input image'
+    model.input_description['iouThreshold'] = f'(optional) IOU Threshold override (default: {nms.iouThreshold})'
+    model.input_description['confidenceThreshold'] = \
+        f'(optional) Confidence Threshold override (default: {nms.confidenceThreshold})'
+    model.output_description['confidence'] = 'Boxes × Class confidence (see user-defined metadata "classes")'
+    model.output_description['coordinates'] = 'Boxes × [x, y, width, height] (relative to image size)'
+    model.save(f)  # pipelined
+    print(f'{prefix} pipeline success ({time.time() - t:.2f}s), saved as {f} ({file_size(f):.1f} MB)')
+
+
 @smart_inference_mode()
 def run(
         data=ROOT / 'data/coco128.yaml',  # 'dataset.yaml path'
@@ -582,9 +771,11 @@ def run(
     if onnx or xml:  # OpenVINO requires ONNX
         f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
     if xml:  # OpenVINO
-        f[3], _ = export_openvino(file, metadata, half)
+        f[3], _ = export_openvino(file, metadata, half, int8, data)
     if coreml:  # CoreML
-        f[4], _ = export_coreml(model, im, file, int8, half)
+        f[4], ct_model = export_coreml(model, im, file, int8, half, nms)
+        if nms:
+            pipeline_coreml(ct_model, im, file, model.names, y)
     if any((saved_model, pb, tflite, edgetpu, tfjs)):  # TensorFlow formats
         assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
         assert not isinstance(model, ClassificationModel), 'ClassificationModel export to TF formats not yet supported.'
@@ -640,7 +831,7 @@ def parse_opt(known=False):
     parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
     parser.add_argument('--keras', action='store_true', help='TF: use Keras')
     parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
-    parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
+    parser.add_argument('--int8', action='store_true', help='CoreML/TF/OpenVINO INT8 quantization')
     parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes')
     parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
     parser.add_argument('--opset', type=int, default=17, help='ONNX: opset version')

models/common.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Common modules
 """
@@ -24,12 +24,24 @@ import torch.nn as nn
 from PIL import Image
 from torch.cuda import amp
 
+# Import 'ultralytics' package or install if if missing
+try:
+    import ultralytics
+
+    assert hasattr(ultralytics, '__version__')  # verify package is not directory
+except (ImportError, AssertionError):
+    import os
+
+    os.system('pip install -U ultralytics')
+    import ultralytics
+
+from ultralytics.utils.plotting import Annotator, colors, save_one_box
+
 from utils import TryExcept
 from utils.dataloaders import exif_transpose, letterbox
 from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
                            increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
                            xyxy2xywh, yaml_load)
-from utils.plots import Annotator, colors, save_one_box
 from utils.torch_utils import copy_attr, smart_inference_mode
 
 
@@ -333,7 +345,7 @@ class DetectMultiBackend(nn.Module):
         super().__init__()
         w = str(weights[0] if isinstance(weights, list) else weights)
         pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
-        fp16 &= pt or jit or onnx or engine  # FP16
+        fp16 &= pt or jit or onnx or engine or triton  # FP16
         nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)
         stride = 32  # default stride
         cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
@@ -353,7 +365,8 @@ class DetectMultiBackend(nn.Module):
             model.half() if fp16 else model.float()
             if extra_files['config.txt']:  # load metadata dict
                 d = json.loads(extra_files['config.txt'],
-                               object_hook=lambda d: {int(k) if k.isdigit() else k: v
+                               object_hook=lambda d: {
+                                   int(k) if k.isdigit() else k: v
                                    for k, v in d.items()})
                 stride, names = int(d['stride']), d['names']
         elif dnn:  # ONNX OpenCV DNN
@@ -372,18 +385,18 @@ class DetectMultiBackend(nn.Module):
                 stride, names = int(meta['stride']), eval(meta['names'])
         elif xml:  # OpenVINO
             LOGGER.info(f'Loading {w} for OpenVINO inference...')
-            check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+            check_requirements('openvino>=2023.0')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
             from openvino.runtime import Core, Layout, get_batch
-            ie = Core()
+            core = Core()
             if not Path(w).is_file():  # if not *.xml
                 w = next(Path(w).glob('*.xml'))  # get *.xml file from *_openvino_model dir
-            network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
+            ov_model = core.read_model(model=w, weights=Path(w).with_suffix('.bin'))
-            if network.get_parameters()[0].get_layout().empty:
+            if ov_model.get_parameters()[0].get_layout().empty:
-                network.get_parameters()[0].set_layout(Layout('NCHW'))
+                ov_model.get_parameters()[0].set_layout(Layout('NCHW'))
-            batch_dim = get_batch(network)
+            batch_dim = get_batch(ov_model)
             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
+            ov_compiled_model = core.compile_model(ov_model, device_name='AUTO')  # AUTO selects best available device
             stride, names = self._load_metadata(Path(w).with_suffix('.yaml'))  # load metadata
         elif engine:  # TensorRT
             LOGGER.info(f'Loading {w} for TensorRT inference...')
@@ -523,7 +536,7 @@ class DetectMultiBackend(nn.Module):
             y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
         elif self.xml:  # OpenVINO
             im = im.cpu().numpy()  # FP32
-            y = list(self.executable_network([im]).values())
+            y = list(self.ov_compiled_model(im).values())
         elif self.engine:  # TensorRT
             if self.dynamic and im.shape != self.bindings['images'].shape:
                 i = self.model.get_binding_index('images')
@@ -540,7 +553,7 @@ class DetectMultiBackend(nn.Module):
         elif self.coreml:  # CoreML
             im = im.cpu().numpy()
             im = Image.fromarray((im[0] * 255).astype('uint8'))
-            # im = im.resize((192, 320), Image.ANTIALIAS)
+            # im = im.resize((192, 320), Image.BILINEAR)
             y = self.model.predict({'image': im})  # coordinates are xywh normalized
             if 'confidence' in y:
                 box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels

models/experimental.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Experimental modules
 """
@@ -87,11 +87,11 @@ def attempt_load(weights, device=None, inplace=True, fuse=True):
 
         model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval())  # model in eval mode
 
-    # Module compatibility updates
+    # Module updates
     for m in model.modules():
         t = type(m)
         if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
-            m.inplace = inplace  # torch 1.7.0 compatibility
+            m.inplace = inplace
             if t is Detect and not isinstance(m.anchor_grid, list):
                 delattr(m, 'anchor_grid')
                 setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)

models/hub/anchors.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 # Default anchors for COCO data
 
 

models/hub/yolov3-spp.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov3-tiny.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov3.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5-bifpn.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5-fpn.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5-p2.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5-p34.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5-p6.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5-p7.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5-panet.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5l6.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5m6.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5n6.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5s-LeakyReLU.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5s-ghost.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5s-transformer.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5s6.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/hub/yolov5x6.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/segment/yolov5l-seg.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/segment/yolov5m-seg.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/segment/yolov5n-seg.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/segment/yolov5s-seg.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/segment/yolov5x-seg.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/tf.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 TensorFlow, Keras and TFLite versions of YOLOv5
 Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127

models/yolo.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 YOLO-specific modules
 
@@ -21,8 +21,8 @@ if str(ROOT) not in sys.path:
 if platform.system() != 'Windows':
     ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
-from models.common import *
+from models.common import *  # noqa
-from models.experimental import *
+from models.experimental import *  # noqa
 from utils.autoanchor import check_anchor_order
 from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
 from utils.plots import feature_visualization

models/yolov5l.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/yolov5m.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/yolov5n.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/yolov5s.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

models/yolov5x.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes

utils/__init__.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 utils/initialization
 """
@@ -54,13 +54,17 @@ def notebook_init(verbose=True):
     import os
     import shutil
 
-    from utils.general import check_font, check_requirements, is_colab
+    from ultralytics.utils.checks import check_requirements
+
+    from utils.general import check_font, is_colab
     from utils.torch_utils import select_device  # imports
 
     check_font()
 
     import psutil
 
+    if check_requirements('wandb', install=False):
+        os.system('pip uninstall -y wandb')  # eliminate unexpected account creation prompt with infinite hang
     if is_colab():
         shutil.rmtree('/content/sample_data', ignore_errors=True)  # remove colab /sample_data directory
 

utils/activations.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Activation functions
 """

utils/augmentations.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Image augmentation functions
 """

utils/autoanchor.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 AutoAnchor utils
 """

utils/autobatch.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Auto-batch utils
 """

utils/callbacks.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Callback utils
 """

utils/dataloaders.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Dataloaders and dataset utils
 """
@@ -36,7 +36,7 @@ from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, c
 from utils.torch_utils import torch_distributed_zero_first
 
 # Parameters
-HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
+HELP_URL = 'See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data'
 IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm'  # include image suffixes
 VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv'  # include video suffixes
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
@@ -355,7 +355,7 @@ class LoadStreams:
             # Start thread to read frames from video stream
             st = f'{i + 1}/{n}: {s}... '
             if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'):  # if source is YouTube video
-                # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc'
+                # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4'
                 check_requirements(('pafy', 'youtube_dl==2020.12.2'))
                 import pafy
                 s = pafy.new(s).getbest(preftype='mp4').url  # YouTube URL

utils/docker/Dockerfile

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 # Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
 # Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
 
@@ -12,7 +12,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria
 ENV DEBIAN_FRONTEND noninteractive
 RUN apt update
 RUN TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
+RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg
 # RUN alias python=python3
 
 # Security updates
@@ -24,14 +24,13 @@ RUN rm -rf /usr/src/app && mkdir -p /usr/src/app
 WORKDIR /usr/src/app
 
 # Copy contents
-# COPY . /usr/src/app  (issues as not a .git directory)
+COPY . /usr/src/app
-RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
 
 # Install pip packages
 COPY requirements.txt .
 RUN python3 -m pip install --upgrade pip wheel
 RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \
-    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2022.3'
+    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0'
     # tensorflow tensorflowjs \
 
 # Set environment variables

utils/docker/Dockerfile-arm64

@@ -1,9 +1,9 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 # Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
 # Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi
 
 # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
-FROM arm64v8/ubuntu:rolling
+FROM arm64v8/ubuntu:22.10
 
 # Downloads to user config dir
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
@@ -12,7 +12,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria
 ENV DEBIAN_FRONTEND noninteractive
 RUN apt update
 RUN TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev
+RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev
 # RUN alias python=python3
 
 # Install pip packages
@@ -27,8 +27,7 @@ RUN mkdir -p /usr/src/app
 WORKDIR /usr/src/app
 
 # Copy contents
-# COPY . /usr/src/app  (issues as not a .git directory)
+COPY . /usr/src/app
-RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
 ENV DEBIAN_FRONTEND teletype
 
 

utils/docker/Dockerfile-cpu

@@ -1,25 +1,27 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 # Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
 # Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments
 
 # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
-FROM ubuntu:rolling
+FROM ubuntu:mantic-20231011
 
 # Downloads to user config dir
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
 
 # Install linux packages
-ENV DEBIAN_FRONTEND noninteractive
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
-RUN apt update
+RUN apt update \
-RUN TZ=Etc/UTC apt install -y tzdata
+    && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
-RUN apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
 # RUN alias python=python3
 
+# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error
+RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED
+
 # Install pip packages
 COPY requirements.txt .
 RUN python3 -m pip install --upgrade pip wheel
 RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \
-    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2022.3' \
+    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' \
     # tensorflow tensorflowjs \
     --extra-index-url https://download.pytorch.org/whl/cpu
 
@@ -28,9 +30,7 @@ RUN mkdir -p /usr/src/app
 WORKDIR /usr/src/app
 
 # Copy contents
-# COPY . /usr/src/app  (issues as not a .git directory)
+COPY . /usr/src/app
-RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
-ENV DEBIAN_FRONTEND teletype
 
 
 # Usage Examples -------------------------------------------------------------------------------------------------------

utils/downloads.py

@@ -1,10 +1,9 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Download utils
 """
 
 import logging
-import os
 import subprocess
 import urllib
 from pathlib import Path

utils/flask_rest_api/example_request.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Perform test request
 """

utils/flask_rest_api/restapi.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Run a Flask REST API exposing one or more YOLOv5s models
 """

utils/general.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 General utils
 """
@@ -36,6 +36,17 @@ import torch
 import torchvision
 import yaml
 
+# Import 'ultralytics' package or install if if missing
+try:
+    import ultralytics
+
+    assert hasattr(ultralytics, '__version__')  # verify package is not directory
+except (ImportError, AssertionError):
+    os.system('pip install -U ultralytics')
+    import ultralytics
+
+from ultralytics.utils.checks import check_requirements
+
 from utils import TryExcept, emojis
 from utils.downloads import curl_download, gsutil_getsize
 from utils.metrics import box_iou, fitness
@@ -58,6 +69,7 @@ pd.options.display.max_columns = 10
 cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
 os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS)  # NumExpr max threads
 os.environ['OMP_NUM_THREADS'] = '1' if platform.system() == 'darwin' else str(NUM_THREADS)  # OpenMP (PyTorch and SciPy)
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # suppress verbose TF compiler warnings in Colab
 
 
 def is_ascii(s=''):
@@ -369,7 +381,7 @@ def check_git_info(path='.'):
         return {'remote': None, 'branch': None, 'commit': None}
 
 
-def check_python(minimum='3.7.0'):
+def check_python(minimum='3.8.0'):
     # Check current python version vs. required python version
     check_version(platform.python_version(), minimum, name='Python ', hard=True)
 
@@ -386,41 +398,6 @@ def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=Fals
     return result
 
 
-@TryExcept()
-def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=''):
-    # Check installed dependencies meet YOLOv5 requirements (pass *.txt file or list of packages or single package str)
-    prefix = colorstr('red', 'bold', 'requirements:')
-    check_python()  # check python version
-    if isinstance(requirements, Path):  # requirements.txt file
-        file = requirements.resolve()
-        assert file.exists(), f'{prefix} {file} not found, check failed.'
-        with file.open() as f:
-            requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude]
-    elif isinstance(requirements, str):
-        requirements = [requirements]
-
-    s = ''
-    n = 0
-    for r in requirements:
-        try:
-            pkg.require(r)
-        except (pkg.VersionConflict, pkg.DistributionNotFound):  # exception if requirements not met
-            s += f'"{r}" '
-            n += 1
-
-    if s and install and AUTOINSTALL:  # check environment variable
-        LOGGER.info(f"{prefix} YOLOv5 requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...")
-        try:
-            # assert check_online(), "AutoUpdate skipped (offline)"
-            LOGGER.info(check_output(f'pip install {s} {cmds}', shell=True).decode())
-            source = file if 'file' in locals() else requirements
-            s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
-                f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
-            LOGGER.info(s)
-        except Exception as e:
-            LOGGER.warning(f'{prefix} ❌ {e}')
-
-
 def check_img_size(imgsz, s=32, floor=0):
     # Verify image size is a multiple of stride s in each dimension
     if isinstance(imgsz, int):  # integer i.e. img_size=640
@@ -1135,6 +1112,7 @@ def imshow(path, im):
     imshow_(path.encode('unicode_escape').decode(), im)
 
 
+if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename:
     cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow  # redefine
 
 # Variables ------------------------------------------------------------------------------------------------------------

utils/google_app_engine/additional_requirements.txt

@@ -1,5 +1,5 @@
 # add these requirements in your app on top of the existing ones
-pip==21.1
+pip==23.3
-Flask==1.0.2
+Flask==2.3.2
 gunicorn==19.10.0
-werkzeug>=2.2.3 # not directly required, pinned by Snyk to avoid a vulnerability
+werkzeug>=3.0.1 # not directly required, pinned by Snyk to avoid a vulnerability

utils/loggers/__init__.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Logging utils
 """
@@ -9,7 +9,6 @@ from pathlib import Path
 
 import pkg_resources as pkg
 import torch
-from torch.utils.tensorboard import SummaryWriter
 
 from utils.general import LOGGER, colorstr, cv2
 from utils.loggers.clearml.clearml_utils import ClearmlLogger
@@ -20,6 +19,11 @@ from utils.torch_utils import de_parallel
 LOGGERS = ('csv', 'tb', 'wandb', 'clearml', 'comet')  # *.csv, TensorBoard, Weights & Biases, ClearML
 RANK = int(os.getenv('RANK', -1))
 
+try:
+    from torch.utils.tensorboard import SummaryWriter
+except ImportError:
+    SummaryWriter = lambda *args: None  # None = SummaryWriter(str)
+
 try:
     import wandb
 
@@ -42,15 +46,15 @@ except (ImportError, AssertionError):
     clearml = None
 
 try:
-    if RANK not in [0, -1]:
+    if RANK in {0, -1}:
-        comet_ml = None
-    else:
         import comet_ml
 
         assert hasattr(comet_ml, '__version__')  # verify package import not local dir
         from utils.loggers.comet import CometLogger
 
-except (ModuleNotFoundError, ImportError, AssertionError):
+    else:
+        comet_ml = None
+except (ImportError, AssertionError):
     comet_ml = None
 
 
@@ -84,10 +88,6 @@ class Loggers():
         self.csv = True  # always log to csv
 
         # Messages
-        if not clearml:
-            prefix = colorstr('ClearML: ')
-            s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML"
-            self.logger.info(s)
         if not comet_ml:
             prefix = colorstr('Comet: ')
             s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet"
@@ -114,7 +114,7 @@ class Loggers():
                 self.clearml = None
                 prefix = colorstr('ClearML: ')
                 LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.'
-                               f' See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme')
+                               f' See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme')
 
         else:
             self.clearml = None

utils/loggers/clearml/clearml_utils.py

@@ -5,8 +5,7 @@ from pathlib import Path
 
 import numpy as np
 import yaml
-
+from ultralytics.utils.plotting import Annotator, colors
-from utils.plots import Annotator, colors
 
 try:
     import clearml

utils/loggers/comet/README.md

@@ -59,7 +59,7 @@ Check out an example of a [completed run here](https://www.comet.com/examples/co
 
 Or better yet, try it out yourself in this Colab Notebook
 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-training/yolov5/notebooks/Comet_and_YOLOv5.ipynb)
 
 # Log automatically
 
@@ -164,7 +164,7 @@ env COMET_LOG_PER_CLASS_METRICS=true python train.py \
 
 If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github), you can do so using the `upload_dataset` flag.
 
-The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/tutorials/train-custom-datasets/#3-organize-directories). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file.
+The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file.
 
 ```shell
 python train.py \

utils/loggers/comet/__init__.py

@@ -18,7 +18,7 @@ try:
     # Project Configuration
     config = comet_ml.config.get_config()
     COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5')
-except (ModuleNotFoundError, ImportError):
+except ImportError:
     comet_ml = None
     COMET_PROJECT_NAME = None
 
@@ -42,7 +42,7 @@ COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5')
 COMET_UPLOAD_DATASET = os.getenv('COMET_UPLOAD_DATASET', 'false').lower() == 'true'
 
 # Evaluation Settings
-COMET_LOG_CONFUSION_MATRIX = os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true'
+COMET_LOG_CONFUSION_MATRIX = (os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true')
 COMET_LOG_PREDICTIONS = os.getenv('COMET_LOG_PREDICTIONS', 'true').lower() == 'true'
 COMET_MAX_IMAGE_UPLOADS = int(os.getenv('COMET_MAX_IMAGE_UPLOADS', 100))
 
@@ -51,10 +51,10 @@ CONF_THRES = float(os.getenv('CONF_THRES', 0.001))
 IOU_THRES = float(os.getenv('IOU_THRES', 0.6))
 
 # Batch Logging Settings
-COMET_LOG_BATCH_METRICS = os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true'
+COMET_LOG_BATCH_METRICS = (os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true')
 COMET_BATCH_LOGGING_INTERVAL = os.getenv('COMET_BATCH_LOGGING_INTERVAL', 1)
 COMET_PREDICTION_LOGGING_INTERVAL = os.getenv('COMET_PREDICTION_LOGGING_INTERVAL', 1)
-COMET_LOG_PER_CLASS_METRICS = os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true'
+COMET_LOG_PER_CLASS_METRICS = (os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true')
 
 RANK = int(os.getenv('RANK', -1))
 
@@ -82,7 +82,7 @@ class CometLogger:
         self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL
 
         # Dataset Artifact Settings
-        self.upload_dataset = self.opt.upload_dataset if self.opt.upload_dataset else COMET_UPLOAD_DATASET
+        self.upload_dataset = self.opt.upload_dataset or COMET_UPLOAD_DATASET
         self.resume = self.opt.resume
 
         # Default parameters to pass to Experiment objects
@@ -93,6 +93,7 @@ class CometLogger:
             'project_name': COMET_PROJECT_NAME, }
         self.default_experiment_kwargs.update(experiment_kwargs)
         self.experiment = self._get_experiment(self.comet_mode, run_id)
+        self.experiment.set_name(self.opt.name)
 
         self.data_dict = self.check_dataset(self.opt.data)
         self.class_names = self.data_dict['names']
@@ -136,7 +137,7 @@ class CometLogger:
 
         self.comet_log_predictions = COMET_LOG_PREDICTIONS
         if self.opt.bbox_interval == -1:
-            self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10
+            self.comet_log_prediction_interval = (1 if self.opt.epochs < 10 else self.opt.epochs // 10)
         else:
             self.comet_log_prediction_interval = self.opt.bbox_interval
 
@@ -231,7 +232,8 @@ class CometLogger:
         with open(data_file) as f:
             data_config = yaml.safe_load(f)
 
-        if data_config['path'].startswith(COMET_PREFIX):
+        path = data_config.get('path')
+        if path and path.startswith(COMET_PREFIX):
             path = data_config['path'].replace(COMET_PREFIX, '')
             data_dict = self.download_dataset_artifact(path)
 
@@ -312,8 +314,16 @@ class CometLogger:
             image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file])
 
             try:
-                artifact.add(image_file, logical_path=image_logical_path, metadata={'split': split})
+                artifact.add(
-                artifact.add(label_file, logical_path=label_logical_path, metadata={'split': split})
+                    image_file,
+                    logical_path=image_logical_path,
+                    metadata={'split': split},
+                )
+                artifact.add(
+                    label_file,
+                    logical_path=label_logical_path,
+                    metadata={'split': split},
+                )
             except ValueError as e:
                 logger.error('COMET ERROR: Error adding file to Artifact. Skipping file.')
                 logger.error(f'COMET ERROR: {e}')
@@ -355,15 +365,14 @@ class CometLogger:
         data_dict['path'] = artifact_save_dir
 
         metadata_names = metadata.get('names')
-        if type(metadata_names) == dict:
+        if isinstance(metadata_names, dict):
             data_dict['names'] = {int(k): v for k, v in metadata.get('names').items()}
-        elif type(metadata_names) == list:
+        elif isinstance(metadata_names, list):
             data_dict['names'] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)}
         else:
             raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary"
 
-        data_dict = self.update_data_paths(data_dict)
+        return self.update_data_paths(data_dict)
-        return data_dict
 
     def update_data_paths(self, data_dict):
         path = data_dict.get('path', '')
@@ -475,8 +484,9 @@ class CometLogger:
                             'f1': f1[i],
                             'true_positives': tp[i],
                             'false_positives': fp[i],
-                            'support': nt[c]},
+                            'support': nt[c], },
-                        prefix=class_name)
+                        prefix=class_name,
+                    )
 
         if self.comet_log_confusion_matrix:
             epoch = self.experiment.curr_epoch

utils/loggers/wandb/wandb_utils.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # WARNING ⚠️ wandb is deprecated and will be removed in future release.
 # See supported integrations at https://github.com/ultralytics/yolov5#integrations

utils/loss.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Loss functions
 """

utils/metrics.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Model validation metrics
 """

utils/plots.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Plotting utils
 """
@@ -8,7 +8,6 @@ import math
 import os
 from copy import copy
 from pathlib import Path
-from urllib.error import URLError
 
 import cv2
 import matplotlib
@@ -17,13 +16,13 @@ import numpy as np
 import pandas as pd
 import seaborn as sn
 import torch
-from PIL import Image, ImageDraw, ImageFont
+from PIL import Image, ImageDraw
+from scipy.ndimage.filters import gaussian_filter1d
+from ultralytics.utils.plotting import Annotator
 
 from utils import TryExcept, threaded
-from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path,
+from utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh
-                           is_ascii, xywh2xyxy, xyxy2xywh)
 from utils.metrics import fitness
-from utils.segment.general import scale_image
 
 # Settings
 RANK = int(os.getenv('RANK', -1))
@@ -52,120 +51,6 @@ class Colors:
 colors = Colors()  # create instance for 'from utils.plots import colors'
 
 
-def check_pil_font(font=FONT, size=10):
-    # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
-    font = Path(font)
-    font = font if font.exists() else (CONFIG_DIR / font.name)
-    try:
-        return ImageFont.truetype(str(font) if font.exists() else font.name, size)
-    except Exception:  # download if missing
-        try:
-            check_font(font)
-            return ImageFont.truetype(str(font), size)
-        except TypeError:
-            check_requirements('Pillow>=8.4.0')  # known issue https://github.com/ultralytics/yolov5/issues/5374
-        except URLError:  # not online
-            return ImageFont.load_default()
-
-
-class Annotator:
-    # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
-    def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
-        assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
-        non_ascii = not is_ascii(example)  # non-latin labels, i.e. asian, arabic, cyrillic
-        self.pil = pil or non_ascii
-        if self.pil:  # use PIL
-            self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
-            self.draw = ImageDraw.Draw(self.im)
-            self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
-                                       size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
-        else:  # use cv2
-            self.im = im
-        self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2)  # line width
-
-    def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
-        # Add one xyxy box to image with label
-        if self.pil or not is_ascii(label):
-            self.draw.rectangle(box, width=self.lw, outline=color)  # box
-            if label:
-                w, h = self.font.getsize(label)  # text width, height (WARNING: deprecated) in 9.2.0
-                # _, _, w, h = self.font.getbbox(label)  # text width, height (New)
-                outside = box[1] - h >= 0  # label fits outside box
-                self.draw.rectangle(
-                    (box[0], box[1] - h if outside else box[1], box[0] + w + 1,
-                     box[1] + 1 if outside else box[1] + h + 1),
-                    fill=color,
-                )
-                # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0
-                self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
-        else:  # cv2
-            p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
-            cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
-            if label:
-                tf = max(self.lw - 1, 1)  # font thickness
-                w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0]  # text width, height
-                outside = p1[1] - h >= 3
-                p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
-                cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
-                cv2.putText(self.im,
-                            label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
-                            0,
-                            self.lw / 3,
-                            txt_color,
-                            thickness=tf,
-                            lineType=cv2.LINE_AA)
-
-    def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
-        """Plot masks at once.
-        Args:
-            masks (tensor): predicted masks on cuda, shape: [n, h, w]
-            colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]
-            im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]
-            alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
-        """
-        if self.pil:
-            # convert to numpy first
-            self.im = np.asarray(self.im).copy()
-        if len(masks) == 0:
-            self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
-        colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
-        colors = colors[:, None, None]  # shape(n,1,1,3)
-        masks = masks.unsqueeze(3)  # shape(n,h,w,1)
-        masks_color = masks * (colors * alpha)  # shape(n,h,w,3)
-
-        inv_alph_masks = (1 - masks * alpha).cumprod(0)  # shape(n,h,w,1)
-        mcs = (masks_color * inv_alph_masks).sum(0) * 2  # mask color summand shape(n,h,w,3)
-
-        im_gpu = im_gpu.flip(dims=[0])  # flip channel
-        im_gpu = im_gpu.permute(1, 2, 0).contiguous()  # shape(h,w,3)
-        im_gpu = im_gpu * inv_alph_masks[-1] + mcs
-        im_mask = (im_gpu * 255).byte().cpu().numpy()
-        self.im[:] = im_mask if retina_masks else scale_image(im_gpu.shape, im_mask, self.im.shape)
-        if self.pil:
-            # convert im back to PIL and update draw
-            self.fromarray(self.im)
-
-    def rectangle(self, xy, fill=None, outline=None, width=1):
-        # Add rectangle to image (PIL-only)
-        self.draw.rectangle(xy, fill, outline, width)
-
-    def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
-        # Add text to image (PIL-only)
-        if anchor == 'bottom':  # start y from font bottom
-            w, h = self.font.getsize(text)  # text width, height
-            xy[1] += 1 - h
-        self.draw.text(xy, text, fill=txt_color, font=self.font)
-
-    def fromarray(self, im):
-        # Update self.im from a numpy array
-        self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
-        self.draw = ImageDraw.Draw(self.im)
-
-    def result(self):
-        # Return annotated image as array
-        return np.asarray(self.im)
-
-
 def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
     """
     x:              Features to be visualized
@@ -265,7 +150,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None):
         x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
         annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2)  # borders
         if paths:
-            annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
+            annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
         if len(targets) > 0:
             ti = targets[targets[:, 0] == i]  # image targets
             boxes = xywh2xyxy(ti[:, 2:6]).T
@@ -500,7 +385,8 @@ def plot_results(file='path/to/results.csv', dir=''):
             for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
                 y = data.values[:, j].astype('float')
                 # y[y == 0] = np.nan  # don't show zero values
-                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
+                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)  # actual results
+                ax[i].plot(x, gaussian_filter1d(y, sigma=3), ':', label='smooth', linewidth=2)  # smoothing line
                 ax[i].set_title(s[j], fontsize=12)
                 # if j in [8, 9, 10]:  # share train and val loss y axes
                 #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])

utils/segment/augmentations.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Image augmentation functions
 """

utils/segment/dataloaders.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Dataloaders
 """

utils/segment/metrics.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Model validation metrics
 """

utils/segment/plots.py

@@ -54,7 +54,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
         x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
         annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2)  # borders
         if paths:
-            annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
+            annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
         if len(targets) > 0:
             idx = targets[:, 0] == i
             ti = targets[idx]  # image targets

utils/torch_utils.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 PyTorch utils
 """

utils/triton.py

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """ Utils to interact with the Triton Inference Server
 """