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Add CoreML iOS updates (#121)
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>
This commit is contained in:
Glenn Jocher
GitHub
parent
fec13ec773
commit
c9f3e469cb
@ -25,7 +25,7 @@ pandas>=1.1.4
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seaborn>=0.11.0
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# Export --------------------------------------
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# coremltools>=5.2 # CoreML export
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# coremltools>=6.0 # CoreML export
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# onnx>=1.12.0 # ONNX export
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# onnx-simplifier>=0.4.1 # ONNX simplifier
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# nvidia-pyindex # TensorRT export
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@ -89,7 +89,7 @@ class DetectionModel(BaseModel):
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LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
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self.yaml['nc'] = nc # override yaml value
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self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch], verbose=verbose) # model, savelist
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self.names = [str(i) for i in range(self.yaml['nc'])] # default names
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self.names = {i: f'{i}' for i in range(self.yaml['nc'])} # default names dict
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self.inplace = self.yaml.get('inplace', True)
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# Build strides
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@ -73,6 +73,7 @@ dynamic: False # ONNX/TF/TensorRT: dynamic axes
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simplify: False # ONNX: simplify model
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opset: 17 # ONNX: opset version
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workspace: 4 # TensorRT: workspace size (GB)
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nms: False # CoreML: add NMS
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# Hyperparameters ------------------------------------------------------------------------------------------------------
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lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
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@ -64,6 +64,7 @@ import numpy as np
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import pandas as pd
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import torch
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import ultralytics
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from ultralytics.nn.modules import Detect, Segment
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from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, attempt_load_weights
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from ultralytics.yolo.configs import get_config
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@ -73,7 +74,7 @@ from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, colorstr, get_default
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from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
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from ultralytics.yolo.utils.files import file_size, increment_path, yaml_save
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from ultralytics.yolo.utils.ops import Profile
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from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode
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from ultralytics.yolo.utils.torch_utils import guess_task_from_head, select_device, smart_inference_mode
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MACOS = platform.system() == 'Darwin' # macOS environment
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@ -119,7 +120,7 @@ class Exporter:
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def __init__(self, config=DEFAULT_CONFIG, overrides={}):
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self.args = get_config(config, overrides)
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project = self.args.project or f"runs/{self.args.task}"
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name = self.args.name or f"{self.args.mode}"
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name = self.args.name or "exp" # hardcode mode as export doesn't require it
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self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
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self.save_dir.mkdir(parents=True, exist_ok=True)
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self.imgsz = self.args.imgsz
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@ -136,22 +137,20 @@ class Exporter:
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# Load PyTorch model
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self.device = select_device(self.args.device)
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if self.args.half:
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assert self.device.type != 'cpu' or coreml, '--half only compatible with GPU export, i.e. use --device 0'
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if self.device.type == 'cpu' or not coreml:
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LOGGER.info('half=True only compatible with GPU or CoreML export, i.e. use device=0 or format=coreml')
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self.args.half = False
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assert not self.args.dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic'
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# Checks
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if isinstance(self.imgsz, int):
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self.imgsz = [self.imgsz]
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self.imgsz *= 2 if len(self.imgsz) == 1 else 1 # expand
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self.imgsz = check_imgsz(self.imgsz, stride=model.stride, min_dim=2) # check image size
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if self.args.optimize:
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assert self.device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
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# Input
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self.args.batch_size = 1 # TODO: resolve this issue, default 16 not fit for export
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gs = int(max(model.stride)) # grid size (max stride)
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imgsz = [check_imgsz(x, gs) for x in self.imgsz] # verify img_size are gs-multiples
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im = torch.zeros(self.args.batch_size, 3, *imgsz).to(self.device) # image size(1,3,320,192) BCHW iDetection
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file = Path(Path(model.yaml['yaml_file']).name)
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im = torch.zeros(self.args.batch_size, 3, *self.imgsz).to(self.device)
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file = Path(getattr(model, 'yaml_file', None) or Path(model.yaml['yaml_file']).name)
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# Update model
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model = deepcopy(model)
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@ -182,7 +181,9 @@ class Exporter:
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self.im = im
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self.model = model
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self.file = file
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self.output_shape = tuple(y.shape)
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self.metadata = {'stride': int(max(model.stride)), 'names': model.names} # model metadata
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self.pretty_name = self.file.stem.replace('yolo', 'YOLO')
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# Exports
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f = [''] * len(fmts) # exported filenames
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@ -202,7 +203,7 @@ class Exporter:
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f[5], s_model = self._export_saved_model(nms=nms or self.args.agnostic_nms or tfjs,
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agnostic_nms=self.args.agnostic_nms or tfjs)
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if pb or tfjs: # pb prerequisite to tfjs
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f[6], _ = self._export_pb(s_model,)
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f[6], _ = self._export_pb(s_model)
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if tflite or edgetpu:
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f[7], _ = self._export_tflite(s_model,
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int8=self.args.int8 or edgetpu,
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@ -220,11 +221,8 @@ class Exporter:
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# Finish
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f = [str(x) for x in f if x] # filter out '' and None
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if any(f):
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cls, det, seg = (isinstance(model, x)
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for x in (ClassificationModel, DetectionModel, SegmentationModel)) # type
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det &= not seg # segmentation models inherit from SegmentationModel(DetectionModel)
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task = guess_task_from_head(model.yaml["head"][-1][-2])
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s = "-WARNING ⚠️ not yet supported for YOLOv8 exported models"
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task = 'detect' if det else 'segment' if seg else 'classify' if cls else ''
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LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
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f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
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f"\nPredict: yolo task={task} mode=predict model={f[-1]} {s}"
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@ -337,13 +335,30 @@ class Exporter:
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@try_export
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def _export_coreml(self, prefix=colorstr('CoreML:')):
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# YOLOv5 CoreML export
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check_requirements('coremltools')
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check_requirements('coremltools>=6.0')
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import coremltools as ct # noqa
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class iOSModel(torch.nn.Module):
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# Wrap an Ultralytics YOLO model for iOS export
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def __init__(self, model, im):
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super().__init__()
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b, c, h, w = im.shape # batch, channel, height, width
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self.model = model
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self.nc = len(model.names) # number of classes
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if w == h:
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self.normalize = 1.0 / w # scalar
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else:
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self.normalize = torch.tensor([1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h]) # broadcast (slower, smaller)
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def forward(self, x):
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xywh, cls = self.model(x)[0].transpose(0, 1).split((4, self.nc), 1)
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return cls, xywh * self.normalize # confidence (3780, 80), coordinates (3780, 4)
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LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
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f = self.file.with_suffix('.mlmodel')
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ts = torch.jit.trace(self.model, self.im, strict=False) # TorchScript model
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model = iOSModel(self.model, self.im) if self.args.nms else self.model
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ts = torch.jit.trace(model, self.im, strict=False) # TorchScript model
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ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=self.im.shape, scale=1 / 255, bias=[0, 0, 0])])
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bits, mode = (8, 'kmeans_lut') if self.args.int8 else (16, 'linear') if self.args.half else (32, None)
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if bits < 32:
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@ -351,6 +366,9 @@ class Exporter:
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ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
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else:
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LOGGER.info(f'{prefix} quantization only supported on macOS, skipping...')
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if self.args.nms:
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ct_model = self._pipeline_coreml(ct_model)
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ct_model.save(str(f))
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return f, ct_model
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@ -525,8 +543,10 @@ class Exporter:
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sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system
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for c in (
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'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
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'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
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'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'):
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'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | ' # no comma
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'sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
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'sudo apt-get update',
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'sudo apt-get install edgetpu-compiler'):
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subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
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ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]
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@ -597,6 +617,127 @@ class Exporter:
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populator.populate()
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tmp_file.unlink()
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def _pipeline_coreml(self, model, prefix=colorstr('CoreML Pipeline:')):
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# YOLOv5 CoreML pipeline
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import coremltools as ct # noqa
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LOGGER.info(f'{prefix} starting pipeline with coremltools {ct.__version__}...')
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batch_size, ch, h, w = list(self.im.shape) # BCHW
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# Output shapes
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spec = model.get_spec()
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out0, out1 = iter(spec.description.output)
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if MACOS:
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from PIL import Image
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img = Image.new('RGB', (w, h)) # img(192 width, 320 height)
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# img = torch.zeros((*opt.img_size, 3)).numpy() # img size(320,192,3) iDetection
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out = model.predict({'image': img})
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out0_shape = out[out0.name].shape
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out1_shape = out[out1.name].shape
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else: # linux and windows can not run model.predict(), get sizes from pytorch output y
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out0_shape = self.output_shape[1], self.output_shape[2] - 5 # (3780, 80)
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out1_shape = self.output_shape[1], 4 # (3780, 4)
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# Checks
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names = self.metadata['names']
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nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height
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na, nc = out0_shape
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# na, nc = out0.type.multiArrayType.shape # number anchors, classes
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assert len(names) == nc, f'{len(names)} names found for nc={nc}' # check
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# Define output shapes (missing)
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out0.type.multiArrayType.shape[:] = out0_shape # (3780, 80)
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out1.type.multiArrayType.shape[:] = out1_shape # (3780, 4)
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# spec.neuralNetwork.preprocessing[0].featureName = '0'
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# Flexible input shapes
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# from coremltools.models.neural_network import flexible_shape_utils
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# s = [] # shapes
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# s.append(flexible_shape_utils.NeuralNetworkImageSize(320, 192))
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# s.append(flexible_shape_utils.NeuralNetworkImageSize(640, 384)) # (height, width)
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# flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='image', sizes=s)
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# r = flexible_shape_utils.NeuralNetworkImageSizeRange() # shape ranges
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# r.add_height_range((192, 640))
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# r.add_width_range((192, 640))
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# flexible_shape_utils.update_image_size_range(spec, feature_name='image', size_range=r)
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# Print
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print(spec.description)
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# Model from spec
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model = ct.models.MLModel(spec)
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# 3. Create NMS protobuf
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nms_spec = ct.proto.Model_pb2.Model()
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nms_spec.specificationVersion = 5
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for i in range(2):
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decoder_output = model._spec.description.output[i].SerializeToString()
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nms_spec.description.input.add()
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nms_spec.description.input[i].ParseFromString(decoder_output)
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nms_spec.description.output.add()
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nms_spec.description.output[i].ParseFromString(decoder_output)
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nms_spec.description.output[0].name = 'confidence'
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nms_spec.description.output[1].name = 'coordinates'
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output_sizes = [nc, 4]
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for i in range(2):
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ma_type = nms_spec.description.output[i].type.multiArrayType
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ma_type.shapeRange.sizeRanges.add()
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ma_type.shapeRange.sizeRanges[0].lowerBound = 0
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ma_type.shapeRange.sizeRanges[0].upperBound = -1
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ma_type.shapeRange.sizeRanges.add()
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ma_type.shapeRange.sizeRanges[1].lowerBound = output_sizes[i]
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ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i]
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del ma_type.shape[:]
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nms = nms_spec.nonMaximumSuppression
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nms.confidenceInputFeatureName = out0.name # 1x507x80
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nms.coordinatesInputFeatureName = out1.name # 1x507x4
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nms.confidenceOutputFeatureName = 'confidence'
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nms.coordinatesOutputFeatureName = 'coordinates'
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nms.iouThresholdInputFeatureName = 'iouThreshold'
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nms.confidenceThresholdInputFeatureName = 'confidenceThreshold'
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nms.iouThreshold = 0.45
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nms.confidenceThreshold = 0.25
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nms.pickTop.perClass = True
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nms.stringClassLabels.vector.extend(names.values())
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nms_model = ct.models.MLModel(nms_spec)
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# 4. Pipeline models together
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pipeline = ct.models.pipeline.Pipeline(input_features=[('image', ct.models.datatypes.Array(3, ny, nx)),
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('iouThreshold', ct.models.datatypes.Double()),
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('confidenceThreshold', ct.models.datatypes.Double())],
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output_features=['confidence', 'coordinates'])
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pipeline.add_model(model)
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pipeline.add_model(nms_model)
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# Correct datatypes
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pipeline.spec.description.input[0].ParseFromString(model._spec.description.input[0].SerializeToString())
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pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString())
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pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString())
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# Update metadata
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pipeline.spec.specificationVersion = 5
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pipeline.spec.description.metadata.versionString = f'Ultralytics YOLOv{ultralytics.__version__}'
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pipeline.spec.description.metadata.shortDescription = f'Ultralytics {self.pretty_name} CoreML model'
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pipeline.spec.description.metadata.author = 'Ultralytics (https://ultralytics.com)'
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pipeline.spec.description.metadata.license = 'GPL-3.0 license (https://ultralytics.com/license)'
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pipeline.spec.description.metadata.userDefined.update({
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'IoU threshold': str(nms.iouThreshold),
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'Confidence threshold': str(nms.confidenceThreshold)})
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# Save the model
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model = ct.models.MLModel(pipeline.spec)
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model.input_description['image'] = 'Input image'
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model.input_description['iouThreshold'] = f'(optional) IOU threshold override (default: {nms.iouThreshold})'
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model.input_description['confidenceThreshold'] = \
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f'(optional) Confidence threshold override (default: {nms.confidenceThreshold})'
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model.output_description['confidence'] = 'Boxes × Class confidence (see user-defined metadata "classes")'
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model.output_description['coordinates'] = 'Boxes × [x, y, width, height] (relative to image size)'
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LOGGER.info(f'{prefix} pipeline success')
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return model
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
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def export(cfg):
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@ -1,5 +1,3 @@
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from pathlib import Path
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import torch
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from ultralytics import yolo # noqa required for python usage
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@ -7,9 +5,9 @@ from ultralytics.nn.tasks import ClassificationModel, DetectionModel, Segmentati
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from ultralytics.yolo.configs import get_config
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from ultralytics.yolo.engine.exporter import Exporter
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from ultralytics.yolo.utils import DEFAULT_CONFIG, HELP_MSG, LOGGER
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from ultralytics.yolo.utils.checks import check_yaml
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from ultralytics.yolo.utils.checks import check_imgsz, check_yaml
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from ultralytics.yolo.utils.files import yaml_load
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from ultralytics.yolo.utils.torch_utils import smart_inference_mode
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from ultralytics.yolo.utils.torch_utils import guess_task_from_head, smart_inference_mode
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# map head: [model, trainer, validator, predictor]
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MODEL_MAP = {
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@ -63,7 +61,7 @@ class YOLO:
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cfg = check_yaml(cfg) # check YAML
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cfg_dict = yaml_load(cfg) # model dict
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obj = cls(init_key=cls.__init_key)
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obj.task = obj._guess_task_from_head(cfg_dict["head"][-1][-2])
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obj.task = guess_task_from_head(cfg_dict["head"][-1][-2])
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obj.ModelClass, obj.TrainerClass, obj.ValidatorClass, obj.PredictorClass = obj._guess_ops_from_task(obj.task)
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obj.model = obj.ModelClass(cfg_dict, verbose=verbose) # initialize
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obj.cfg = cfg
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@ -132,13 +130,7 @@ class YOLO:
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overrides["mode"] = "predict"
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predictor = self.PredictorClass(overrides=overrides)
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# check size type
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sz = predictor.args.imgsz
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if type(sz) != int: # received listConfig
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predictor.args.imgsz = [sz[0], sz[0]] if len(sz) == 1 else [sz[0], sz[1]] # expand
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else:
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predictor.args.imgsz = [sz, sz]
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predictor.args.imgsz = check_imgsz(predictor.args.imgsz, min_dim=2) # check image size
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predictor.setup(model=self.model, source=source)
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predictor()
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@ -179,7 +171,7 @@ class YOLO:
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args = get_config(config=DEFAULT_CONFIG, overrides=overrides)
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args.task = self.task
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exporter = Exporter(overrides=overrides)
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exporter = Exporter(overrides=args)
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exporter(model=self.model)
|
||||
|
||||
def train(self, **kwargs):
|
||||
@ -230,21 +222,6 @@ class YOLO:
|
||||
|
||||
self.trainer.train()
|
||||
|
||||
@staticmethod
|
||||
def _guess_task_from_head(head):
|
||||
task = None
|
||||
if head.lower() in ["classify", "classifier", "cls", "fc"]:
|
||||
task = "classify"
|
||||
if head.lower() in ["detect"]:
|
||||
task = "detect"
|
||||
if head.lower() in ["segment"]:
|
||||
task = "segment"
|
||||
|
||||
if not task:
|
||||
raise SyntaxError("task or model not recognized! Please refer the docs at : ") # TODO: add docs links
|
||||
|
||||
return task
|
||||
|
||||
def to(self, device):
|
||||
self.model.to(device)
|
||||
|
||||
|
||||
@ -35,9 +35,9 @@ from ultralytics.yolo.configs import get_config
|
||||
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadScreenshots, LoadStreams
|
||||
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
|
||||
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, colorstr, ops
|
||||
from ultralytics.yolo.utils.checks import check_file, check_imshow
|
||||
from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_imshow
|
||||
from ultralytics.yolo.utils.files import increment_path
|
||||
from ultralytics.yolo.utils.torch_utils import check_imgsz, select_device, smart_inference_mode
|
||||
from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode
|
||||
|
||||
|
||||
class BasePredictor:
|
||||
@ -90,7 +90,7 @@ class BasePredictor:
|
||||
self.args.half &= device.type != 'cpu' # half precision only supported on CUDA
|
||||
model = AutoBackend(model, device=device, dnn=self.args.dnn, fp16=self.args.half)
|
||||
stride, pt = model.stride, model.pt
|
||||
imgsz = check_imgsz(self.args.imgsz, s=stride) # check image size
|
||||
imgsz = check_imgsz(self.args.imgsz, stride=stride) # check image size
|
||||
|
||||
# Dataloader
|
||||
bs = 1 # batch_size
|
||||
|
||||
@ -14,7 +14,7 @@ import numpy as np
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
from omegaconf import OmegaConf
|
||||
from omegaconf import OmegaConf # noqa
|
||||
from torch.cuda import amp
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.optim import lr_scheduler
|
||||
|
||||
@ -2,15 +2,16 @@ import json
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from omegaconf import OmegaConf
|
||||
from omegaconf import OmegaConf # noqa
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics.nn.autobackend import AutoBackend
|
||||
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
|
||||
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, RANK, TQDM_BAR_FORMAT
|
||||
from ultralytics.yolo.utils.checks import check_imgsz
|
||||
from ultralytics.yolo.utils.files import increment_path
|
||||
from ultralytics.yolo.utils.ops import Profile
|
||||
from ultralytics.yolo.utils.torch_utils import check_imgsz, de_parallel, select_device, smart_inference_mode
|
||||
from ultralytics.yolo.utils.torch_utils import de_parallel, select_device, smart_inference_mode
|
||||
|
||||
|
||||
class BaseValidator:
|
||||
@ -60,7 +61,7 @@ class BaseValidator:
|
||||
model = AutoBackend(model, device=self.device, dnn=self.args.dnn, fp16=self.args.half)
|
||||
self.model = model
|
||||
stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
|
||||
imgsz = check_imgsz(self.args.imgsz, s=stride)
|
||||
imgsz = check_imgsz(self.args.imgsz, stride=stride)
|
||||
if engine:
|
||||
self.args.batch_size = model.batch_size
|
||||
else:
|
||||
|
||||
@ -22,16 +22,26 @@ def is_ascii(s=''):
|
||||
return len(s.encode().decode('ascii', 'ignore')) == len(s)
|
||||
|
||||
|
||||
def check_imgsz(imgsz, s=32, floor=0):
|
||||
def check_imgsz(imgsz, stride=32, min_dim=1, floor=0):
|
||||
# Verify image size is a multiple of stride s in each dimension
|
||||
if isinstance(imgsz, int): # integer i.e. img_size=640
|
||||
new_size = max(make_divisible(imgsz, int(s)), floor)
|
||||
else: # list i.e. img_size=[640, 480]
|
||||
|
||||
stride = int(stride.max() if isinstance(stride, torch.Tensor) else stride)
|
||||
if isinstance(imgsz, int): # integer i.e. imgsz=640
|
||||
sz = max(make_divisible(imgsz, stride), floor)
|
||||
else: # list i.e. imgsz=[640, 480]
|
||||
imgsz = list(imgsz) # convert to list if tuple
|
||||
new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
|
||||
if new_size != imgsz:
|
||||
LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
|
||||
return new_size
|
||||
sz = [max(make_divisible(x, stride), floor) for x in imgsz]
|
||||
if sz != imgsz:
|
||||
LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {stride}, updating to {sz}')
|
||||
|
||||
# Check dims
|
||||
if min_dim == 2:
|
||||
if isinstance(imgsz, int):
|
||||
sz = [sz, sz]
|
||||
elif len(sz) == 1:
|
||||
sz = [sz[0], sz[0]]
|
||||
|
||||
return sz
|
||||
|
||||
|
||||
def check_version(current="0.0.0", minimum="0.0.0", name="version ", pinned=False, hard=False, verbose=False):
|
||||
|
||||
@ -185,18 +185,6 @@ def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416)
|
||||
return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean
|
||||
|
||||
|
||||
def check_imgsz(imgsz, s=32, floor=0):
|
||||
# Verify image size is a multiple of stride s in each dimension
|
||||
if isinstance(imgsz, int): # integer i.e. imgsz=640
|
||||
new_size = max(make_divisible(imgsz, int(s)), floor)
|
||||
else: # list i.e. imgsz=[640, 480]
|
||||
imgsz = list(imgsz) # convert to list if tuple
|
||||
new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
|
||||
if new_size != imgsz:
|
||||
LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
|
||||
return new_size
|
||||
|
||||
|
||||
def make_divisible(x, divisor):
|
||||
# Returns nearest x divisible by divisor
|
||||
if isinstance(divisor, torch.Tensor):
|
||||
@ -293,3 +281,18 @@ def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_op
|
||||
torch.save(x, s or f)
|
||||
mb = os.path.getsize(s or f) / 1E6 # filesize
|
||||
LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")
|
||||
|
||||
|
||||
def guess_task_from_head(head):
|
||||
task = None
|
||||
if head.lower() in ["classify", "classifier", "cls", "fc"]:
|
||||
task = "classify"
|
||||
if head.lower() in ["detect"]:
|
||||
task = "detect"
|
||||
if head.lower() in ["segment"]:
|
||||
task = "segment"
|
||||
|
||||
if not task:
|
||||
raise SyntaxError("task or model not recognized! Please refer the docs at : ") # TODO: add docs links
|
||||
|
||||
return task
|
||||
|
||||
@ -3,6 +3,7 @@ import torch
|
||||
|
||||
from ultralytics.yolo.engine.predictor import BasePredictor
|
||||
from ultralytics.yolo.utils import DEFAULT_CONFIG
|
||||
from ultralytics.yolo.utils.checks import check_imgsz
|
||||
from ultralytics.yolo.utils.plotting import Annotator
|
||||
|
||||
|
||||
@ -54,11 +55,7 @@ class ClassificationPredictor(BasePredictor):
|
||||
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
|
||||
def predict(cfg):
|
||||
cfg.model = cfg.model or "squeezenet1_0"
|
||||
sz = cfg.imgsz
|
||||
if type(sz) != int: # received listConfig
|
||||
cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]] # expand
|
||||
else:
|
||||
cfg.imgsz = [sz, sz]
|
||||
cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2) # check image size
|
||||
predictor = ClassificationPredictor(cfg)
|
||||
predictor()
|
||||
|
||||
|
||||
@ -3,6 +3,7 @@ import torch
|
||||
|
||||
from ultralytics.yolo.engine.predictor import BasePredictor
|
||||
from ultralytics.yolo.utils import DEFAULT_CONFIG, ops
|
||||
from ultralytics.yolo.utils.checks import check_imgsz
|
||||
from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
|
||||
|
||||
|
||||
@ -83,11 +84,7 @@ class DetectionPredictor(BasePredictor):
|
||||
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
|
||||
def predict(cfg):
|
||||
cfg.model = cfg.model or "n.pt"
|
||||
sz = cfg.imgsz
|
||||
if type(sz) != int: # received listConfig
|
||||
cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]] # expand
|
||||
else:
|
||||
cfg.imgsz = [sz, sz]
|
||||
cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2) # check image size
|
||||
predictor = DetectionPredictor(cfg)
|
||||
predictor()
|
||||
|
||||
|
||||
@ -2,6 +2,7 @@ import hydra
|
||||
import torch
|
||||
|
||||
from ultralytics.yolo.utils import DEFAULT_CONFIG, ops
|
||||
from ultralytics.yolo.utils.checks import check_imgsz
|
||||
from ultralytics.yolo.utils.plotting import colors, save_one_box
|
||||
|
||||
from ..detect.predict import DetectionPredictor
|
||||
@ -96,11 +97,7 @@ class SegmentationPredictor(DetectionPredictor):
|
||||
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
|
||||
def predict(cfg):
|
||||
cfg.model = cfg.model or "n.pt"
|
||||
sz = cfg.imgsz
|
||||
if type(sz) != int: # received listConfig
|
||||
cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]] # expand
|
||||
else:
|
||||
cfg.imgsz = [sz, sz]
|
||||
cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2) # check image size
|
||||
predictor = SegmentationPredictor(cfg)
|
||||
predictor()
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user