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default.yaml type comments (#3237)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher
GitHub
parent
21df296425
commit
e78fb683f4
@ -2,8 +2,8 @@
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# YOLOv6 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/models/yolov6
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# Parameters
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act: nn.ReLU()
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nc: 80 # number of classes
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activation: nn.ReLU() # (optional) model default activation function
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scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call yolov8.yaml with scale 'n'
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# [depth, width, max_channels]
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n: [0.33, 0.25, 1024]
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@ -422,9 +422,7 @@ class RTDETRDetectionModel(DetectionModel):
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# NOTE: preprocess gt_bbox and gt_labels to list.
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bs = len(img)
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batch_idx = batch['batch_idx']
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gt_groups = []
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for i in range(bs):
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gt_groups.append((batch_idx == i).sum().item())
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gt_groups = [(batch_idx == i).sum().item() for i in range(bs)]
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targets = {
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'cls': batch['cls'].to(img.device, dtype=torch.long).view(-1),
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'bboxes': batch['bboxes'].to(device=img.device),
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@ -606,7 +604,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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# Args
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max_channels = float('inf')
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nc, act, scales = (d.get(x) for x in ('nc', 'act', 'scales'))
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nc, act, scales = (d.get(x) for x in ('nc', 'activation', 'scales'))
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depth, width, kpt_shape = (d.get(x, 1.0) for x in ('depth_multiple', 'width_multiple', 'kpt_shape'))
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if scales:
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scale = d.get('scale')
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@ -22,20 +22,15 @@ class MaskDecoder(nn.Module):
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iou_head_hidden_dim: int = 256,
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) -> None:
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"""
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Predicts masks given an image and prompt embeddings, using a
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transformer architecture.
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Predicts masks given an image and prompt embeddings, using a transformer architecture.
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Arguments:
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transformer_dim (int): the channel dimension of the transformer
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transformer (nn.Module): the transformer used to predict masks
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num_multimask_outputs (int): the number of masks to predict
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when disambiguating masks
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activation (nn.Module): the type of activation to use when
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upscaling masks
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iou_head_depth (int): the depth of the MLP used to predict
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mask quality
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iou_head_hidden_dim (int): the hidden dimension of the MLP
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used to predict mask quality
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transformer_dim (int): the channel dimension of the transformer module
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transformer (nn.Module): the transformer used to predict masks
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num_multimask_outputs (int): the number of masks to predict when disambiguating masks
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activation (nn.Module): the type of activation to use when upscaling masks
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iou_head_depth (int): the depth of the MLP used to predict mask quality
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iou_head_hidden_dim (int): the hidden dimension of the MLP used to predict mask quality
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"""
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super().__init__()
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self.transformer_dim = transformer_dim
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@ -71,16 +66,15 @@ class MaskDecoder(nn.Module):
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Predict masks given image and prompt embeddings.
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Arguments:
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image_embeddings (torch.Tensor): the embeddings from the image encoder
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image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
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sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
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dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
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multimask_output (bool): Whether to return multiple masks or a single
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mask.
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image_embeddings (torch.Tensor): the embeddings from the image encoder
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image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
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sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
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dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
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multimask_output (bool): Whether to return multiple masks or a single mask.
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Returns:
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torch.Tensor: batched predicted masks
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torch.Tensor: batched predictions of mask quality
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torch.Tensor: batched predicted masks
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torch.Tensor: batched predictions of mask quality
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"""
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masks, iou_pred = self.predict_masks(
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image_embeddings=image_embeddings,
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@ -136,9 +130,11 @@ class MaskDecoder(nn.Module):
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return masks, iou_pred
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# Lightly adapted from
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# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
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class MLP(nn.Module):
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"""
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Lightly adapted from
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https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py
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"""
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def __init__(
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self,
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@ -249,7 +249,7 @@ def get_cdn_group(batch,
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attn_mask[max_nums * 2 * i:max_nums * 2 * (i + 1), max_nums * 2 * (i + 1):num_dn] = True
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attn_mask[max_nums * 2 * i:max_nums * 2 * (i + 1), :max_nums * 2 * i] = True
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dn_meta = {
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'dn_pos_idx': [p.reshape(-1) for p in pos_idx.cpu().split([n for n in gt_groups], dim=1)],
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'dn_pos_idx': [p.reshape(-1) for p in pos_idx.cpu().split(list(gt_groups), dim=1)],
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'dn_num_group': num_group,
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'dn_num_split': [num_dn, num_queries]}
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@ -258,5 +258,6 @@ def get_cdn_group(batch,
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def inverse_sigmoid(x, eps=1e-6):
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"""Inverse sigmoid function."""
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x = x.clip(min=0., max=1.)
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return torch.log(x / (1 - x + eps) + eps)
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@ -1,117 +1,117 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Default training settings and hyperparameters for medium-augmentation COCO training
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task: detect # YOLO task, i.e. detect, segment, classify, pose
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mode: train # YOLO mode, i.e. train, val, predict, export, track, benchmark
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task: detect # (str) YOLO task, i.e. detect, segment, classify, pose
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mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
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# Train settings -------------------------------------------------------------------------------------------------------
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model: # path to model file, i.e. yolov8n.pt, yolov8n.yaml
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data: # path to data file, 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 (-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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save_period: -1 # Save checkpoint every x epochs (disabled if < 1)
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cache: False # True/ram, disk or False. Use cache for data loading
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device: # 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: # project name
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name: # experiment name, results saved to 'project/name' directory
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exist_ok: False # whether to overwrite existing experiment
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pretrained: False # whether to use a pretrained model
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optimizer: auto # optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
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verbose: True # whether to print verbose output
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seed: 0 # random seed for reproducibility
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deterministic: True # whether to enable deterministic mode
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single_cls: False # train multi-class data as single-class
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rect: False # rectangular training if mode='train' or rectangular validation if mode='val'
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cos_lr: False # use cosine learning rate scheduler
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model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
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data: # (str, optional) path to data file, i.e. coco128.yaml
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epochs: 100 # (int) number of epochs to train for
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patience: 50 # (int) epochs to wait for no observable improvement for early stopping of training
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batch: 16 # (int) number of images per batch (-1 for AutoBatch)
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imgsz: 640 # (int) size of input images as integer or w,h
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save: True # (bool) save train checkpoints and predict results
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save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1)
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cache: False # (bool) True/ram, disk or False. Use cache for data loading
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device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
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workers: 8 # (int) number of worker threads for data loading (per RANK if DDP)
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project: # (str, optional) project name
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name: # (str, optional) experiment name, results saved to 'project/name' directory
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exist_ok: False # (bool) whether to overwrite existing experiment
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pretrained: False # (bool) whether to use a pretrained model
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optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
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verbose: True # (bool) whether to print verbose output
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seed: 0 # (int) random seed for reproducibility
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deterministic: True # (bool) whether to enable deterministic mode
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single_cls: False # (bool) train multi-class data as single-class
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rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
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cos_lr: False # (bool) use cosine learning rate scheduler
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close_mosaic: 0 # (int) disable mosaic augmentation for final epochs
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resume: False # resume training from last checkpoint
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amp: True # Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
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fraction: 1.0 # dataset fraction to train on (default is 1.0, all images in train set)
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profile: False # profile ONNX and TensorRT speeds during training for loggers
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resume: False # (bool) resume training from last checkpoint
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amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
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fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
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profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
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# Segmentation
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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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overlap_mask: True # (bool) masks should overlap during training (segment train only)
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mask_ratio: 4 # (int) mask downsample ratio (segment train only)
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# Classification
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dropout: 0.0 # use dropout regularization (classify train only)
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dropout: 0.0 # (float) 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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split: val # dataset split to use for validation, i.e. 'val', 'test' or 'train'
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save_json: False # save results to JSON file
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save_hybrid: False # save hybrid version of labels (labels + additional predictions)
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conf: # object confidence threshold for detection (default 0.25 predict, 0.001 val)
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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 # save plots during train/val
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val: True # (bool) validate/test during training
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split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
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save_json: False # (bool) save results to JSON file
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save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions)
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conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
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iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
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max_det: 300 # (int) maximum number of detections per image
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half: False # (bool) use half precision (FP16)
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dnn: False # (bool) use OpenCV DNN for ONNX inference
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plots: True # (bool) save plots during train/val
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# Prediction settings --------------------------------------------------------------------------------------------------
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source: # source directory for images or videos
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show: False # show results if possible
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save_txt: False # save results as .txt file
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save_conf: False # save results with confidence scores
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save_crop: False # save cropped images with results
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show_labels: True # show object labels in plots
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show_conf: True # show object confidence scores in plots
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vid_stride: 1 # video frame-rate stride
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line_width: # line width of the bounding boxes
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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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classes: # filter results by class, i.e. class=0, or class=[0,2,3]
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retina_masks: False # use high-resolution segmentation masks
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boxes: True # Show boxes in segmentation predictions
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source: # (str, optional) source directory for images or videos
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show: False # (bool) show results if possible
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save_txt: False # (bool) save results as .txt file
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save_conf: False # (bool) save results with confidence scores
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save_crop: False # (bool) save cropped images with results
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show_labels: True # (bool) show object labels in plots
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show_conf: True # (bool) show object confidence scores in plots
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vid_stride: 1 # (int) video frame-rate stride
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line_width: # (int, optional) line width of the bounding boxes, auto if missing
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visualize: False # (bool) visualize model features
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augment: False # (bool) apply image augmentation to prediction sources
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agnostic_nms: False # (bool) class-agnostic NMS
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classes: # (int | list[int], optional) filter results by class, i.e. class=0, or class=[0,2,3]
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retina_masks: False # (bool) use high-resolution segmentation masks
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boxes: True # (bool) Show boxes in segmentation predictions
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# Export settings ------------------------------------------------------------------------------------------------------
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format: torchscript # format to export to
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keras: False # use Keras
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optimize: False # TorchScript: optimize for mobile
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int8: False # CoreML/TF INT8 quantization
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dynamic: False # ONNX/TF/TensorRT: dynamic axes
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simplify: False # ONNX: simplify model
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opset: # ONNX: opset version (optional)
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workspace: 4 # TensorRT: workspace size (GB)
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nms: False # CoreML: add NMS
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format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
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keras: False # (bool) use Kera=s
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optimize: False # (bool) TorchScript: optimize for mobile
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int8: False # (bool) CoreML/TF INT8 quantization
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dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
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simplify: False # (bool) ONNX: simplify model
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opset: # (int, optional) ONNX: opset version
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workspace: 4 # (int) TensorRT: workspace size (GB)
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nms: False # (bool) CoreML: add NMS
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# Hyperparameters ------------------------------------------------------------------------------------------------------
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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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warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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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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pose: 12.0 # pose loss gain
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kobj: 1.0 # keypoint obj loss gain
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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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hsv_v: 0.4 # image HSV-Value augmentation (fraction)
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degrees: 0.0 # image rotation (+/- deg)
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translate: 0.1 # image translation (+/- fraction)
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scale: 0.5 # image scale (+/- gain)
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shear: 0.0 # image shear (+/- deg)
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perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
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flipud: 0.0 # image flip up-down (probability)
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fliplr: 0.5 # image flip left-right (probability)
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mosaic: 1.0 # image mosaic (probability)
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mixup: 0.0 # image mixup (probability)
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copy_paste: 0.0 # segment copy-paste (probability)
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lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
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lrf: 0.01 # (float) final learning rate (lr0 * lrf)
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momentum: 0.937 # (float) SGD momentum/Adam beta1
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weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
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warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
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warmup_momentum: 0.8 # (float) warmup initial momentum
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warmup_bias_lr: 0.1 # (float) warmup initial bias lr
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box: 7.5 # (float) box loss gain
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cls: 0.5 # (float) cls loss gain (scale with pixels)
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dfl: 1.5 # (float) dfl loss gain
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pose: 12.0 # (float) pose loss gain
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kobj: 1.0 # (float) keypoint obj loss gain
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label_smoothing: 0.0 # (float) label smoothing (fraction)
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nbs: 64 # (int) nominal batch size
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hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
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hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
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hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
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degrees: 0.0 # (float) image rotation (+/- deg)
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translate: 0.1 # (float) image translation (+/- fraction)
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scale: 0.5 # (float) image scale (+/- gain)
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shear: 0.0 # (float) image shear (+/- deg)
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perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
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flipud: 0.0 # (float) image flip up-down (probability)
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fliplr: 0.5 # (float) image flip left-right (probability)
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mosaic: 1.0 # (float) image mosaic (probability)
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mixup: 0.0 # (float) image mixup (probability)
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copy_paste: 0.0 # (float) segment copy-paste (probability)
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# Custom config.yaml ---------------------------------------------------------------------------------------------------
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cfg: # for overriding defaults.yaml
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cfg: # (str, optional) for overriding defaults.yaml
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# Debug, do not modify -------------------------------------------------------------------------------------------------
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v5loader: False # use legacy YOLOv5 dataloader
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v5loader: False # (bool) use legacy YOLOv5 dataloader (deprecated)
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# Tracker settings ------------------------------------------------------------------------------------------------------
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tracker: botsort.yaml # tracker type, ['botsort.yaml', 'bytetrack.yaml']
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tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]
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