ultralytics 8.0.14 Hydra removal fixes and cleanup (#542)
Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Kamlesh Kumar <patelkamleshpatel364@gmail.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -28,7 +28,7 @@ CLI_HELP_MSG = \
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Where TASK (optional) is one of [detect, segment, classify]
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MODE (required) is one of [train, val, predict, export]
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ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
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For a full list of available ARGS see https://docs.ultralytics.com/config.
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For a full list of available ARGS see https://docs.ultralytics.com/cfg.
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Train a detection model for 10 epochs with an initial learning_rate of 0.01
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
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@ -48,7 +48,7 @@ CLI_HELP_MSG = \
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yolo checks
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yolo version
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yolo settings
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yolo copy-config
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yolo copy-cfg
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Docs: https://docs.ultralytics.com/cli
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Community: https://community.ultralytics.com
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@ -56,6 +56,15 @@ CLI_HELP_MSG = \
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"""
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class UltralyticsCFG(SimpleNamespace):
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"""
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UltralyticsCFG iterable SimpleNamespace class to allow SimpleNamespace to be used with dict() and in for loops
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"""
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def __iter__(self):
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return iter(vars(self).items())
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def cfg2dict(cfg):
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"""
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Convert a configuration object to a dictionary.
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@ -75,30 +84,30 @@ def cfg2dict(cfg):
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return cfg
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def get_config(config: Union[str, Path, Dict, SimpleNamespace], overrides: Dict = None):
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def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace], overrides: Dict = None):
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"""
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Load and merge configuration data from a file or dictionary.
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Args:
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config (str) or (Path) or (Dict) or (SimpleNamespace): Configuration data.
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cfg (str) or (Path) or (Dict) or (SimpleNamespace): Configuration data.
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overrides (str) or (Dict), optional: Overrides in the form of a file name or a dictionary. Default is None.
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Returns:
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(SimpleNamespace): Training arguments namespace.
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"""
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config = cfg2dict(config)
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cfg = cfg2dict(cfg)
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# Merge overrides
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if overrides:
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overrides = cfg2dict(overrides)
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check_config_mismatch(config, overrides)
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config = {**config, **overrides} # merge config and overrides dicts (prefer overrides)
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check_cfg_mismatch(cfg, overrides)
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cfg = {**cfg, **overrides} # merge cfg and overrides dicts (prefer overrides)
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# Return instance
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return SimpleNamespace(**config)
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return UltralyticsCFG(**cfg)
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def check_config_mismatch(base: Dict, custom: Dict):
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def check_cfg_mismatch(base: Dict, custom: Dict):
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"""
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This function checks for any mismatched keys between a custom configuration list and a base configuration list.
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If any mismatched keys are found, the function prints out similar keys from the base list and exits the program.
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@ -127,8 +136,8 @@ def entrypoint(debug=False):
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- running special modes like 'checks'
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- passing overrides to the package's configuration
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It uses the package's default config and initializes it using the passed overrides.
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Then it calls the CLI function with the composed config
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It uses the package's default cfg and initializes it using the passed overrides.
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Then it calls the CLI function with the composed cfg
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"""
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if debug:
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args = ['train', 'predict', 'model=yolov8n.pt'] # for testing
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@ -149,7 +158,7 @@ def entrypoint(debug=False):
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'checks': checks.check_yolo,
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'version': lambda: LOGGER.info(__version__),
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'settings': print_settings,
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'copy-config': copy_default_config}
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'copy-cfg': copy_default_config}
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overrides = {} # basic overrides, i.e. imgsz=320
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defaults = yaml_load(DEFAULT_CFG_PATH)
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@ -190,7 +199,7 @@ def entrypoint(debug=False):
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f"https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/configs/default.yaml"
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f"\n{CLI_HELP_MSG}")
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cfg = get_config(defaults, overrides) # create CFG instance
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cfg = get_cfg(defaults, overrides) # create CFG instance
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# Mapping from task to module
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module = {"detect": yolo.v8.detect, "segment": yolo.v8.segment, "classify": yolo.v8.classify}.get(cfg.task)
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@ -214,7 +223,7 @@ def copy_default_config():
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new_file = Path.cwd() / DEFAULT_CFG_PATH.name.replace('.yaml', '_copy.yaml')
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shutil.copy2(DEFAULT_CFG_PATH, new_file)
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LOGGER.info(f"{PREFIX}{DEFAULT_CFG_PATH} copied to {new_file}\n"
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f"Usage for running YOLO with this new custom config:\nyolo cfg={new_file} args...")
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f"Usage for running YOLO with this new custom cfg:\nyolo cfg={new_file} args...")
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if __name__ == '__main__':
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@ -1,20 +1,20 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Default training settings and hyperparameters for medium-augmentation COCO training
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task: "detect" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case. Specify task to run.
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mode: "train" # choices=['train', 'val', 'predict'] # mode to run task in.
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task: "detect" # inference task, i.e. detect, segment, classify
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mode: "train" # YOLO mode, i.e. train, val, predict, export
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# Train settings -------------------------------------------------------------------------------------------------------
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model: null # i.e. yolov8n.pt, yolov8n.yaml. Path to model file
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data: null # i.e. coco128.yaml. Path to data file
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model: null # path to model file, i.e. yolov8n.pt, yolov8n.yaml
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data: null # path to data file, i.e. i.e. coco128.yaml
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epochs: 100 # number of epochs to train for
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patience: 50 # epochs to wait for no observable improvement for early stopping of training
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batch: 16 # number of images per batch
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imgsz: 640 # size of input images
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save: True # save checkpoints
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batch: 16 # number of images per batch (-1 for AutoBatch)
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imgsz: 640 # size of input images as integer or w,h
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save: True # save train checkpoints and predict results
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cache: False # True/ram, disk or False. Use cache for data loading
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device: null # cuda device, i.e. 0 or 0,1,2,3 or cpu. Device to run on
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workers: 8 # number of worker threads for data loading
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device: null # device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
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workers: 8 # number of worker threads for data loading (per RANK if DDP)
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project: null # project name
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name: null # experiment name
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exist_ok: False # whether to overwrite existing experiment
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@ -30,10 +30,10 @@ cos_lr: False # use cosine learning rate scheduler
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close_mosaic: 10 # disable mosaic augmentation for final 10 epochs
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resume: False # resume training from last checkpoint
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# Segmentation
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overlap_mask: True # masks should overlap during training
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mask_ratio: 4 # mask downsample ratio
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overlap_mask: True # masks should overlap during training (segment train only)
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mask_ratio: 4 # mask downsample ratio (segment train only)
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# Classification
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dropout: 0.0 # use dropout regularization
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dropout: 0.0 # use dropout regularization (classify train only)
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# Val/Test settings ----------------------------------------------------------------------------------------------------
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val: True # validate/test during training
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@ -44,7 +44,7 @@ iou: 0.7 # intersection over union (IoU) threshold for NMS
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max_det: 300 # maximum number of detections per image
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half: False # use half precision (FP16)
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dnn: False # use OpenCV DNN for ONNX inference
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plots: True # show plots during training
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plots: True # save plots during train/val
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# Prediction settings --------------------------------------------------------------------------------------------------
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source: null # source directory for images or videos
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@ -56,10 +56,11 @@ hide_labels: False # hide labels
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hide_conf: False # hide confidence scores
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vid_stride: 1 # video frame-rate stride
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line_thickness: 3 # bounding box thickness (pixels)
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visualize: False # visualize results
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augment: False # apply data augmentation to images
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visualize: False # visualize model features
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augment: False # apply image augmentation to prediction sources
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agnostic_nms: False # class-agnostic NMS
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retina_masks: False # use retina masks for object detection
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retina_masks: False # use high-resolution segmentation masks
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classes: null # filter results by class, i.e. class=0, or class=[0,2,3]
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# Export settings ------------------------------------------------------------------------------------------------------
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format: torchscript # format to export to
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@ -73,8 +74,8 @@ 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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lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
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lr0: 0.01 # initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
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lrf: 0.01 # final learning rate (lr0 * lrf)
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momentum: 0.937 # SGD momentum/Adam beta1
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weight_decay: 0.0005 # optimizer weight decay 5e-4
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warmup_epochs: 3.0 # warmup epochs (fractions ok)
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@ -84,7 +85,7 @@ box: 7.5 # box loss gain
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cls: 0.5 # cls loss gain (scale with pixels)
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dfl: 1.5 # dfl loss gain
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fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
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label_smoothing: 0.0
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label_smoothing: 0.0 # label smoothing (fraction)
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nbs: 64 # nominal batch size
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hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
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hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
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@ -615,7 +615,7 @@ class LoadImagesAndLabels(Dataset):
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nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages
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desc = f"{prefix}Scanning {path.parent / path.stem}..."
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total = len(self.im_files)
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with (Pool if total > 10000 else ThreadPool)(NUM_THREADS) as pool:
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with ThreadPool(NUM_THREADS) as pool:
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results = pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix)))
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pbar = tqdm(results, desc=desc, total=total, bar_format=TQDM_BAR_FORMAT)
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for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
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@ -1,7 +1,7 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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from itertools import repeat
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from multiprocessing.pool import Pool, ThreadPool
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from multiprocessing.pool import ThreadPool
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from pathlib import Path
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import torchvision
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@ -51,7 +51,7 @@ class YOLODataset(BaseDataset):
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nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages
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desc = f"{self.prefix}Scanning {path.parent / path.stem}..."
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total = len(self.im_files)
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with (Pool if total > 10000 else ThreadPool)(NUM_THREADS) as pool:
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with ThreadPool(NUM_THREADS) as pool:
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results = pool.imap(func=verify_image_label,
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iterable=zip(self.im_files, self.label_files, repeat(self.prefix),
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repeat(self.use_keypoints)))
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@ -67,7 +67,7 @@ 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
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from ultralytics.yolo.configs import get_config
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
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from ultralytics.yolo.data.utils import check_dataset
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from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, callbacks, colorstr, get_default_args, yaml_save
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@ -134,7 +134,7 @@ class Exporter:
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config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
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overrides (dict, optional): Configuration overrides. Defaults to None.
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"""
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self.args = get_config(config, overrides)
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self.args = get_cfg(config, overrides)
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self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
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callbacks.add_integration_callbacks(self)
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@ -4,7 +4,7 @@ from pathlib import Path
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from ultralytics import yolo # noqa
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from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight
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from ultralytics.yolo.configs import get_config
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.engine.exporter import Exporter
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from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, yaml_load
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from ultralytics.yolo.utils.checks import check_yaml
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@ -136,7 +136,7 @@ class YOLO:
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self.predictor = self.PredictorClass(overrides=overrides)
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self.predictor.setup_model(model=self.model)
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else: # only update args if predictor is already setup
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self.predictor.args = get_config(self.predictor.args, overrides)
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self.predictor.args = get_cfg(self.predictor.args, overrides)
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return self.predictor(source=source, stream=stream, verbose=verbose)
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@smart_inference_mode()
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@ -151,7 +151,7 @@ class YOLO:
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overrides = self.overrides.copy()
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overrides.update(kwargs)
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overrides["mode"] = "val"
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args = get_config(config=DEFAULT_CFG_PATH, overrides=overrides)
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args = get_cfg(cfg=DEFAULT_CFG_PATH, overrides=overrides)
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args.data = data or args.data
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args.task = self.task
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@ -169,7 +169,7 @@ class YOLO:
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overrides = self.overrides.copy()
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overrides.update(kwargs)
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args = get_config(config=DEFAULT_CFG_PATH, overrides=overrides)
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args = get_cfg(cfg=DEFAULT_CFG_PATH, overrides=overrides)
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args.task = self.task
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print(args)
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@ -201,7 +201,7 @@ class YOLO:
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self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
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self.model = self.trainer.model
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self.trainer.train()
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# update model and configs after training
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# update model and cfg after training
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self.model, _ = attempt_load_one_weight(str(self.trainer.best))
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self.overrides = self.model.args
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@ -33,7 +33,7 @@ from pathlib import Path
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import cv2
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from ultralytics.nn.autobackend import AutoBackend
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from ultralytics.yolo.configs import get_config
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadPilAndNumpy, LoadScreenshots, LoadStreams
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from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
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from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, SETTINGS, callbacks, colorstr, ops
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@ -70,7 +70,7 @@ class BasePredictor:
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config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
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overrides (dict, optional): Configuration overrides. Defaults to None.
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"""
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self.args = get_config(config, overrides)
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self.args = get_cfg(config, overrides)
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project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
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name = self.args.name or f"{self.args.mode}"
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self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
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@ -84,6 +84,7 @@ class BasePredictor:
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self.bs = None
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self.imgsz = None
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self.device = None
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self.classes = self.args.classes
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self.dataset = None
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self.vid_path, self.vid_writer = None, None
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self.annotator = None
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@ -100,7 +101,7 @@ class BasePredictor:
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def write_results(self, results, batch, print_string):
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raise NotImplementedError("print_results function needs to be implemented")
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def postprocess(self, preds, img, orig_img):
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def postprocess(self, preds, img, orig_img, classes=None):
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return preds
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def setup_source(self, source=None):
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@ -195,7 +196,7 @@ class BasePredictor:
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# postprocess
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with self.dt[2]:
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results = self.postprocess(preds, im, im0s)
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results = self.postprocess(preds, im, im0s, self.classes)
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for i in range(len(im)):
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p, im0 = (path[i], im0s[i]) if self.webcam or self.from_img else (path, im0s)
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p = Path(p)
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@ -21,6 +21,8 @@ class Results:
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masks (Masks, optional): A Masks object containing the detection masks.
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probs (torch.Tensor, optional): A tensor containing the detection class probabilities.
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orig_shape (tuple, optional): Original image size.
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data (torch.Tensor): The raw masks tensor
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"""
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def __init__(self, boxes=None, masks=None, probs=None, orig_shape=None) -> None:
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@ -81,19 +83,20 @@ class Results:
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return len(getattr(self, item))
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def __str__(self):
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return self.__repr__()
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str_out = ""
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for item in self.comp:
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if getattr(self, item) is None:
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continue
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str_out = str_out + getattr(self, item).__str__()
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return str_out
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def __repr__(self):
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s = f'Ultralytics YOLO {self.__class__} instance\n' # string
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if self.boxes is not None:
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s = s + self.boxes.__repr__() + '\n'
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if self.masks is not None:
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s = s + self.masks.__repr__() + '\n'
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if self.probs is not None:
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s = s + self.probs.__repr__()
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s += f'original size: {self.orig_shape}\n'
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return s
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str_out = ""
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for item in self.comp:
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if getattr(self, item) is None:
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continue
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str_out = str_out + getattr(self, item).__repr__()
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return str_out
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def __getattr__(self, attr):
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name = self.__class__.__name__
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@ -129,6 +132,7 @@ class Boxes:
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xywh (torch.Tensor) or (numpy.ndarray): The boxes in xywh format.
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xyxyn (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format normalized by original image size.
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xywhn (torch.Tensor) or (numpy.ndarray): The boxes in xywh format normalized by original image size.
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||||
data (torch.Tensor): The raw bboxes tensor
|
||||
"""
|
||||
|
||||
def __init__(self, boxes, orig_shape) -> None:
|
||||
@ -198,15 +202,19 @@ class Boxes:
|
||||
def shape(self):
|
||||
return self.boxes.shape
|
||||
|
||||
@property
|
||||
def data(self):
|
||||
return self.boxes
|
||||
|
||||
def __len__(self): # override len(results)
|
||||
return len(self.boxes)
|
||||
|
||||
def __str__(self):
|
||||
return self.__repr__()
|
||||
return self.boxes.__str__()
|
||||
|
||||
def __repr__(self):
|
||||
return (f"Ultralytics YOLO {self.__class__} masks\n" + f"type: {type(self.boxes)}\n" +
|
||||
f"shape: {self.boxes.shape}\n" + f"dtype: {self.boxes.dtype}")
|
||||
f"shape: {self.boxes.shape}\n" + f"dtype: {self.boxes.dtype}\n + {self.boxes.__repr__()}")
|
||||
|
||||
def __getitem__(self, idx):
|
||||
boxes = self.boxes[idx]
|
||||
@ -257,12 +265,16 @@ class Masks:
|
||||
def segments(self):
|
||||
return [
|
||||
ops.scale_segments(self.masks.shape[1:], x, self.orig_shape, normalize=True)
|
||||
for x in reversed(ops.masks2segments(self.masks))]
|
||||
for x in ops.masks2segments(self.masks)]
|
||||
|
||||
@property
|
||||
def shape(self):
|
||||
return self.masks.shape
|
||||
|
||||
@property
|
||||
def data(self):
|
||||
return self.masks
|
||||
|
||||
def cpu(self):
|
||||
masks = self.masks.cpu()
|
||||
return Masks(masks, self.orig_shape)
|
||||
@ -283,11 +295,11 @@ class Masks:
|
||||
return len(self.masks)
|
||||
|
||||
def __str__(self):
|
||||
return self.__repr__()
|
||||
return self.masks.__str__()
|
||||
|
||||
def __repr__(self):
|
||||
return (f"Ultralytics YOLO {self.__class__} masks\n" + f"type: {type(self.masks)}\n" +
|
||||
f"shape: {self.masks.shape}\n" + f"dtype: {self.masks.dtype}")
|
||||
f"shape: {self.masks.shape}\n" + f"dtype: {self.masks.dtype}\n + {self.masks.__repr__()}")
|
||||
|
||||
def __getitem__(self, idx):
|
||||
masks = self.masks[idx]
|
||||
|
||||
@ -23,7 +23,7 @@ from tqdm import tqdm
|
||||
import ultralytics.yolo.utils as utils
|
||||
from ultralytics import __version__
|
||||
from ultralytics.nn.tasks import attempt_load_one_weight
|
||||
from ultralytics.yolo.configs import get_config
|
||||
from ultralytics.yolo.cfg import get_cfg
|
||||
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
|
||||
from ultralytics.yolo.utils import (DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr,
|
||||
yaml_save)
|
||||
@ -79,7 +79,7 @@ class BaseTrainer:
|
||||
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
|
||||
overrides (dict, optional): Configuration overrides. Defaults to None.
|
||||
"""
|
||||
self.args = get_config(config, overrides)
|
||||
self.args = get_cfg(config, overrides)
|
||||
self.device = utils.torch_utils.select_device(self.args.device, self.args.batch)
|
||||
self.check_resume()
|
||||
self.console = LOGGER
|
||||
@ -509,7 +509,7 @@ class BaseTrainer:
|
||||
assert args_yaml.is_file(), \
|
||||
FileNotFoundError('Resume checkpoint f{last} not found. '
|
||||
'Please pass a valid checkpoint to resume from, i.e. yolo resume=path/to/last.pt')
|
||||
args = get_config(args_yaml) # replace
|
||||
args = get_cfg(args_yaml) # replace
|
||||
args.model, resume = str(last), True # reinstate
|
||||
self.args = args
|
||||
self.resume = resume
|
||||
|
||||
@ -8,7 +8,7 @@ import torch
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics.nn.autobackend import AutoBackend
|
||||
from ultralytics.yolo.configs import get_config
|
||||
from ultralytics.yolo.cfg import get_cfg
|
||||
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
|
||||
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks
|
||||
from ultralytics.yolo.utils.checks import check_imgsz
|
||||
@ -52,7 +52,7 @@ class BaseValidator:
|
||||
self.dataloader = dataloader
|
||||
self.pbar = pbar
|
||||
self.logger = logger or LOGGER
|
||||
self.args = args or get_config(DEFAULT_CFG_PATH)
|
||||
self.args = args or get_cfg(DEFAULT_CFG_PATH)
|
||||
self.model = None
|
||||
self.data = None
|
||||
self.device = None
|
||||
|
||||
@ -23,7 +23,7 @@ import yaml
|
||||
# Constants
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[2] # YOLO
|
||||
DEFAULT_CFG_PATH = ROOT / "yolo/configs/default.yaml"
|
||||
DEFAULT_CFG_PATH = ROOT / "yolo/cfg/default.yaml"
|
||||
RANK = int(os.getenv('RANK', -1))
|
||||
NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads
|
||||
AUTOINSTALL = str(os.getenv('YOLO_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode
|
||||
|
||||
@ -26,7 +26,7 @@ def on_pretrain_routine_start(trainer):
|
||||
output_uri=True,
|
||||
reuse_last_task_id=False,
|
||||
auto_connect_frameworks={'pytorch': False})
|
||||
task.connect(dict(trainer.args), name='General')
|
||||
task.connect(vars(trainer.args), name='General')
|
||||
|
||||
|
||||
def on_train_epoch_end(trainer):
|
||||
|
||||
@ -11,7 +11,7 @@ except (ModuleNotFoundError, ImportError):
|
||||
|
||||
def on_pretrain_routine_start(trainer):
|
||||
experiment = comet_ml.Experiment(project_name=trainer.args.project or "YOLOv8")
|
||||
experiment.log_parameters(dict(trainer.args))
|
||||
experiment.log_parameters(vars(trainer.args))
|
||||
|
||||
|
||||
def on_train_epoch_end(trainer):
|
||||
|
||||
@ -137,9 +137,10 @@ def model_info(model, verbose=False, imgsz=640):
|
||||
(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
|
||||
|
||||
flops = get_flops(model, imgsz)
|
||||
fused = ' (fused)' if model.is_fused() else ''
|
||||
fs = f', {flops:.1f} GFLOPs' if flops else ''
|
||||
m = Path(getattr(model, 'yaml_file', '') or model.yaml.get('yaml_file', '')).stem.replace('yolo', 'YOLO') or 'Model'
|
||||
LOGGER.info(f"{m} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
|
||||
LOGGER.info(f"{m} summary{fused}: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
|
||||
|
||||
|
||||
def get_num_params(model):
|
||||
|
||||
@ -18,7 +18,7 @@ class ClassificationPredictor(BasePredictor):
|
||||
img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
|
||||
return img
|
||||
|
||||
def postprocess(self, preds, img, orig_img):
|
||||
def postprocess(self, preds, img, orig_img, classes=None):
|
||||
results = []
|
||||
for i, pred in enumerate(preds):
|
||||
shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
|
||||
|
||||
@ -19,12 +19,13 @@ class DetectionPredictor(BasePredictor):
|
||||
img /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
return img
|
||||
|
||||
def postprocess(self, preds, img, orig_img):
|
||||
def postprocess(self, preds, img, orig_img, classes=None):
|
||||
preds = ops.non_max_suppression(preds,
|
||||
self.args.conf,
|
||||
self.args.iou,
|
||||
agnostic=self.args.agnostic_nms,
|
||||
max_det=self.args.max_det)
|
||||
max_det=self.args.max_det,
|
||||
classes=self.args.classes)
|
||||
|
||||
results = []
|
||||
for i, pred in enumerate(preds):
|
||||
|
||||
@ -10,14 +10,15 @@ from ultralytics.yolo.v8.detect.predict import DetectionPredictor
|
||||
|
||||
class SegmentationPredictor(DetectionPredictor):
|
||||
|
||||
def postprocess(self, preds, img, orig_img):
|
||||
def postprocess(self, preds, img, orig_img, classes=None):
|
||||
# TODO: filter by classes
|
||||
p = ops.non_max_suppression(preds[0],
|
||||
self.args.conf,
|
||||
self.args.iou,
|
||||
agnostic=self.args.agnostic_nms,
|
||||
max_det=self.args.max_det,
|
||||
nm=32)
|
||||
nm=32,
|
||||
classes=self.args.classes)
|
||||
results = []
|
||||
proto = preds[1][-1]
|
||||
for i, pred in enumerate(p):
|
||||
|
||||
Reference in New Issue
Block a user