1860 lines
71 KiB
Python
1860 lines
71 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
|
|
|
import contextlib
|
|
import pickle
|
|
import re
|
|
import types
|
|
from copy import deepcopy
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
from ultralytics.nn.autobackend import check_class_names
|
|
from ultralytics.nn.modules import (
|
|
AIFI,
|
|
C1,
|
|
C2,
|
|
C2PSA,
|
|
C3,
|
|
C3TR,
|
|
ELAN1,
|
|
OBB,
|
|
PSA,
|
|
SPP,
|
|
SPPELAN,
|
|
SPPF,
|
|
A2C2f,
|
|
AConv,
|
|
ADown,
|
|
Bottleneck,
|
|
BottleneckCSP,
|
|
C2f,
|
|
C2fAttn,
|
|
C2fCIB,
|
|
C2fPSA,
|
|
C3Ghost,
|
|
C3k2,
|
|
C3x,
|
|
CBFuse,
|
|
CBLinear,
|
|
Classify,
|
|
Concat,
|
|
Conv,
|
|
Conv2,
|
|
ConvTranspose,
|
|
Detect,
|
|
DWConv,
|
|
DWConvTranspose2d,
|
|
Focus,
|
|
GhostBottleneck,
|
|
GhostConv,
|
|
HGBlock,
|
|
HGStem,
|
|
ImagePoolingAttn,
|
|
Index,
|
|
LRPCHead,
|
|
Pose,
|
|
RepC3,
|
|
RepConv,
|
|
RepNCSPELAN4,
|
|
RepVGGDW,
|
|
ResNetLayer,
|
|
RTDETRDecoder,
|
|
SCDown,
|
|
Segment,
|
|
TorchVision,
|
|
WorldDetect,
|
|
YOLOEDetect,
|
|
YOLOESegment,
|
|
v10Detect,
|
|
)
|
|
from ultralytics.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, YAML, colorstr, emojis
|
|
from ultralytics.utils.checks import check_requirements, check_suffix, check_yaml
|
|
from ultralytics.utils.loss import (
|
|
E2EDetectLoss,
|
|
v8ClassificationLoss,
|
|
v8DetectionLoss,
|
|
v8OBBLoss,
|
|
v8PoseLoss,
|
|
v8SegmentationLoss,
|
|
)
|
|
from ultralytics.utils.ops import make_divisible
|
|
from ultralytics.utils.plotting import feature_visualization
|
|
from ultralytics.utils.torch_utils import (
|
|
fuse_conv_and_bn,
|
|
fuse_deconv_and_bn,
|
|
initialize_weights,
|
|
intersect_dicts,
|
|
model_info,
|
|
scale_img,
|
|
smart_inference_mode,
|
|
time_sync,
|
|
)
|
|
|
|
|
|
class BaseModel(torch.nn.Module):
|
|
"""
|
|
Base class for all YOLO models in the Ultralytics family.
|
|
|
|
This class provides common functionality for YOLO models including forward pass handling, model fusion,
|
|
information display, and weight loading capabilities.
|
|
|
|
Attributes:
|
|
model (torch.nn.Module): The neural network model.
|
|
save (list): List of layer indices to save outputs from.
|
|
stride (torch.Tensor): Model stride values.
|
|
|
|
Methods:
|
|
forward: Perform forward pass for training or inference.
|
|
predict: Perform inference on input tensor.
|
|
fuse: Fuse Conv2d and BatchNorm2d layers for optimization.
|
|
info: Print model information.
|
|
load: Load weights into the model.
|
|
loss: Compute loss for training.
|
|
|
|
Examples:
|
|
Create a BaseModel instance
|
|
>>> model = BaseModel()
|
|
>>> model.info() # Display model information
|
|
"""
|
|
|
|
def forward(self, x, *args, **kwargs):
|
|
"""
|
|
Perform forward pass of the model for either training or inference.
|
|
|
|
If x is a dict, calculates and returns the loss for training. Otherwise, returns predictions for inference.
|
|
|
|
Args:
|
|
x (torch.Tensor | dict): Input tensor for inference, or dict with image tensor and labels for training.
|
|
*args (Any): Variable length argument list.
|
|
**kwargs (Any): Arbitrary keyword arguments.
|
|
|
|
Returns:
|
|
(torch.Tensor): Loss if x is a dict (training), or network predictions (inference).
|
|
"""
|
|
if isinstance(x, dict): # for cases of training and validating while training.
|
|
return self.loss(x, *args, **kwargs)
|
|
return self.predict(x, *args, **kwargs)
|
|
|
|
def predict(self, x, profile=False, visualize=False, augment=False, embed=None):
|
|
"""
|
|
Perform a forward pass through the network.
|
|
|
|
Args:
|
|
x (torch.Tensor): The input tensor to the model.
|
|
profile (bool): Print the computation time of each layer if True.
|
|
visualize (bool): Save the feature maps of the model if True.
|
|
augment (bool): Augment image during prediction.
|
|
embed (list, optional): A list of feature vectors/embeddings to return.
|
|
|
|
Returns:
|
|
(torch.Tensor): The last output of the model.
|
|
"""
|
|
if augment:
|
|
return self._predict_augment(x)
|
|
return self._predict_once(x, profile, visualize, embed)
|
|
|
|
def _predict_once(self, x, profile=False, visualize=False, embed=None):
|
|
"""
|
|
Perform a forward pass through the network.
|
|
|
|
Args:
|
|
x (torch.Tensor): The input tensor to the model.
|
|
profile (bool): Print the computation time of each layer if True.
|
|
visualize (bool): Save the feature maps of the model if True.
|
|
embed (list, optional): A list of feature vectors/embeddings to return.
|
|
|
|
Returns:
|
|
(torch.Tensor): The last output of the model.
|
|
"""
|
|
y, dt, embeddings = [], [], [] # outputs
|
|
embed = frozenset(embed) if embed is not None else {-1}
|
|
max_idx = max(embed)
|
|
for m in self.model:
|
|
if m.f != -1: # if not from previous layer
|
|
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
|
|
if profile:
|
|
self._profile_one_layer(m, x, dt)
|
|
x = m(x) # run
|
|
y.append(x if m.i in self.save else None) # save output
|
|
if visualize:
|
|
feature_visualization(x, m.type, m.i, save_dir=visualize)
|
|
if m.i in embed:
|
|
embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
|
|
if m.i == max_idx:
|
|
return torch.unbind(torch.cat(embeddings, 1), dim=0)
|
|
return x
|
|
|
|
def _predict_augment(self, x):
|
|
"""Perform augmentations on input image x and return augmented inference."""
|
|
LOGGER.warning(
|
|
f"{self.__class__.__name__} does not support 'augment=True' prediction. "
|
|
f"Reverting to single-scale prediction."
|
|
)
|
|
return self._predict_once(x)
|
|
|
|
def _profile_one_layer(self, m, x, dt):
|
|
"""
|
|
Profile the computation time and FLOPs of a single layer of the model on a given input.
|
|
|
|
Args:
|
|
m (torch.nn.Module): The layer to be profiled.
|
|
x (torch.Tensor): The input data to the layer.
|
|
dt (list): A list to store the computation time of the layer.
|
|
"""
|
|
try:
|
|
import thop
|
|
except ImportError:
|
|
thop = None # conda support without 'ultralytics-thop' installed
|
|
|
|
c = m == self.model[-1] and isinstance(x, list) # is final layer list, copy input as inplace fix
|
|
flops = thop.profile(m, inputs=[x.copy() if c else x], verbose=False)[0] / 1e9 * 2 if thop else 0 # GFLOPs
|
|
t = time_sync()
|
|
for _ in range(10):
|
|
m(x.copy() if c else x)
|
|
dt.append((time_sync() - t) * 100)
|
|
if m == self.model[0]:
|
|
LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module")
|
|
LOGGER.info(f"{dt[-1]:10.2f} {flops:10.2f} {m.np:10.0f} {m.type}")
|
|
if c:
|
|
LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
|
|
|
|
def fuse(self, verbose=True):
|
|
"""
|
|
Fuse the `Conv2d()` and `BatchNorm2d()` layers of the model into a single layer for improved computation
|
|
efficiency.
|
|
|
|
Returns:
|
|
(torch.nn.Module): The fused model is returned.
|
|
"""
|
|
if not self.is_fused():
|
|
for m in self.model.modules():
|
|
if isinstance(m, (Conv, Conv2, DWConv)) and hasattr(m, "bn"):
|
|
if isinstance(m, Conv2):
|
|
m.fuse_convs()
|
|
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
|
|
delattr(m, "bn") # remove batchnorm
|
|
m.forward = m.forward_fuse # update forward
|
|
if isinstance(m, ConvTranspose) and hasattr(m, "bn"):
|
|
m.conv_transpose = fuse_deconv_and_bn(m.conv_transpose, m.bn)
|
|
delattr(m, "bn") # remove batchnorm
|
|
m.forward = m.forward_fuse # update forward
|
|
if isinstance(m, RepConv):
|
|
m.fuse_convs()
|
|
m.forward = m.forward_fuse # update forward
|
|
if isinstance(m, RepVGGDW):
|
|
m.fuse()
|
|
m.forward = m.forward_fuse
|
|
if isinstance(m, v10Detect):
|
|
m.fuse() # remove one2many head
|
|
self.info(verbose=verbose)
|
|
|
|
return self
|
|
|
|
def is_fused(self, thresh=10):
|
|
"""
|
|
Check if the model has less than a certain threshold of BatchNorm layers.
|
|
|
|
Args:
|
|
thresh (int, optional): The threshold number of BatchNorm layers.
|
|
|
|
Returns:
|
|
(bool): True if the number of BatchNorm layers in the model is less than the threshold, False otherwise.
|
|
"""
|
|
bn = tuple(v for k, v in torch.nn.__dict__.items() if "Norm" in k) # normalization layers, i.e. BatchNorm2d()
|
|
return sum(isinstance(v, bn) for v in self.modules()) < thresh # True if < 'thresh' BatchNorm layers in model
|
|
|
|
def info(self, detailed=False, verbose=True, imgsz=640):
|
|
"""
|
|
Print model information.
|
|
|
|
Args:
|
|
detailed (bool): If True, prints out detailed information about the model.
|
|
verbose (bool): If True, prints out the model information.
|
|
imgsz (int): The size of the image that the model will be trained on.
|
|
"""
|
|
return model_info(self, detailed=detailed, verbose=verbose, imgsz=imgsz)
|
|
|
|
def _apply(self, fn):
|
|
"""
|
|
Apply a function to all tensors in the model that are not parameters or registered buffers.
|
|
|
|
Args:
|
|
fn (function): The function to apply to the model.
|
|
|
|
Returns:
|
|
(BaseModel): An updated BaseModel object.
|
|
"""
|
|
self = super()._apply(fn)
|
|
m = self.model[-1] # Detect()
|
|
if isinstance(
|
|
m, Detect
|
|
): # includes all Detect subclasses like Segment, Pose, OBB, WorldDetect, YOLOEDetect, YOLOESegment
|
|
m.stride = fn(m.stride)
|
|
m.anchors = fn(m.anchors)
|
|
m.strides = fn(m.strides)
|
|
return self
|
|
|
|
def load(self, weights, verbose=True):
|
|
"""
|
|
Load weights into the model.
|
|
|
|
Args:
|
|
weights (dict | torch.nn.Module): The pre-trained weights to be loaded.
|
|
verbose (bool, optional): Whether to log the transfer progress.
|
|
"""
|
|
model = weights["model"] if isinstance(weights, dict) else weights # torchvision models are not dicts
|
|
csd = model.float().state_dict() # checkpoint state_dict as FP32
|
|
updated_csd = intersect_dicts(csd, self.state_dict()) # intersect
|
|
self.load_state_dict(updated_csd, strict=False) # load
|
|
len_updated_csd = len(updated_csd)
|
|
first_conv = "model.0.conv.weight" # hard-coded to yolo models for now
|
|
# mostly used to boost multi-channel training
|
|
state_dict = self.state_dict()
|
|
if first_conv not in updated_csd and first_conv in state_dict:
|
|
c1, c2, h, w = state_dict[first_conv].shape
|
|
cc1, cc2, ch, cw = csd[first_conv].shape
|
|
if ch == h and cw == w:
|
|
c1, c2 = min(c1, cc1), min(c2, cc2)
|
|
state_dict[first_conv][:c1, :c2] = csd[first_conv][:c1, :c2]
|
|
len_updated_csd += 1
|
|
if verbose:
|
|
LOGGER.info(f"Transferred {len_updated_csd}/{len(self.model.state_dict())} items from pretrained weights")
|
|
|
|
def loss(self, batch, preds=None):
|
|
"""
|
|
Compute loss.
|
|
|
|
Args:
|
|
batch (dict): Batch to compute loss on.
|
|
preds (torch.Tensor | List[torch.Tensor], optional): Predictions.
|
|
"""
|
|
if getattr(self, "criterion", None) is None:
|
|
self.criterion = self.init_criterion()
|
|
|
|
preds = self.forward(batch["img"]) if preds is None else preds
|
|
return self.criterion(preds, batch)
|
|
|
|
def init_criterion(self):
|
|
"""Initialize the loss criterion for the BaseModel."""
|
|
raise NotImplementedError("compute_loss() needs to be implemented by task heads")
|
|
|
|
|
|
class DetectionModel(BaseModel):
|
|
"""
|
|
YOLO detection model.
|
|
|
|
This class implements the YOLO detection architecture, handling model initialization, forward pass,
|
|
augmented inference, and loss computation for object detection tasks.
|
|
|
|
Attributes:
|
|
yaml (dict): Model configuration dictionary.
|
|
model (torch.nn.Sequential): The neural network model.
|
|
save (list): List of layer indices to save outputs from.
|
|
names (dict): Class names dictionary.
|
|
inplace (bool): Whether to use inplace operations.
|
|
end2end (bool): Whether the model uses end-to-end detection.
|
|
stride (torch.Tensor): Model stride values.
|
|
|
|
Methods:
|
|
__init__: Initialize the YOLO detection model.
|
|
_predict_augment: Perform augmented inference.
|
|
_descale_pred: De-scale predictions following augmented inference.
|
|
_clip_augmented: Clip YOLO augmented inference tails.
|
|
init_criterion: Initialize the loss criterion.
|
|
|
|
Examples:
|
|
Initialize a detection model
|
|
>>> model = DetectionModel("yolo11n.yaml", ch=3, nc=80)
|
|
>>> results = model.predict(image_tensor)
|
|
"""
|
|
|
|
def __init__(self, cfg="yolo11n.yaml", ch=3, nc=None, verbose=True):
|
|
"""
|
|
Initialize the YOLO detection model with the given config and parameters.
|
|
|
|
Args:
|
|
cfg (str | dict): Model configuration file path or dictionary.
|
|
ch (int): Number of input channels.
|
|
nc (int, optional): Number of classes.
|
|
verbose (bool): Whether to display model information.
|
|
"""
|
|
super().__init__()
|
|
self.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg) # cfg dict
|
|
if self.yaml["backbone"][0][2] == "Silence":
|
|
LOGGER.warning(
|
|
"YOLOv9 `Silence` module is deprecated in favor of torch.nn.Identity. "
|
|
"Please delete local *.pt file and re-download the latest model checkpoint."
|
|
)
|
|
self.yaml["backbone"][0][2] = "nn.Identity"
|
|
|
|
# Define model
|
|
self.yaml["channels"] = ch # save channels
|
|
if nc and nc != self.yaml["nc"]:
|
|
LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
|
|
self.yaml["nc"] = nc # override YAML value
|
|
self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose) # model, savelist
|
|
self.names = {i: f"{i}" for i in range(self.yaml["nc"])} # default names dict
|
|
self.inplace = self.yaml.get("inplace", True)
|
|
self.end2end = getattr(self.model[-1], "end2end", False)
|
|
|
|
# Build strides
|
|
m = self.model[-1] # Detect()
|
|
if isinstance(m, Detect): # includes all Detect subclasses like Segment, Pose, OBB, YOLOEDetect, YOLOESegment
|
|
s = 256 # 2x min stride
|
|
m.inplace = self.inplace
|
|
|
|
def _forward(x):
|
|
"""Perform a forward pass through the model, handling different Detect subclass types accordingly."""
|
|
if self.end2end:
|
|
return self.forward(x)["one2many"]
|
|
return self.forward(x)[0] if isinstance(m, (Segment, YOLOESegment, Pose, OBB)) else self.forward(x)
|
|
|
|
self.model.eval() # Avoid changing batch statistics until training begins
|
|
m.training = True # Setting it to True to properly return strides
|
|
m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))]) # forward
|
|
self.stride = m.stride
|
|
self.model.train() # Set model back to training(default) mode
|
|
m.bias_init() # only run once
|
|
else:
|
|
self.stride = torch.Tensor([32]) # default stride for i.e. RTDETR
|
|
|
|
# Init weights, biases
|
|
initialize_weights(self)
|
|
if verbose:
|
|
self.info()
|
|
LOGGER.info("")
|
|
|
|
def _predict_augment(self, x):
|
|
"""
|
|
Perform augmentations on input image x and return augmented inference and train outputs.
|
|
|
|
Args:
|
|
x (torch.Tensor): Input image tensor.
|
|
|
|
Returns:
|
|
(torch.Tensor): Augmented inference output.
|
|
"""
|
|
if getattr(self, "end2end", False) or self.__class__.__name__ != "DetectionModel":
|
|
LOGGER.warning("Model does not support 'augment=True', reverting to single-scale prediction.")
|
|
return self._predict_once(x)
|
|
img_size = x.shape[-2:] # height, width
|
|
s = [1, 0.83, 0.67] # scales
|
|
f = [None, 3, None] # flips (2-ud, 3-lr)
|
|
y = [] # outputs
|
|
for si, fi in zip(s, f):
|
|
xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
|
|
yi = super().predict(xi)[0] # forward
|
|
yi = self._descale_pred(yi, fi, si, img_size)
|
|
y.append(yi)
|
|
y = self._clip_augmented(y) # clip augmented tails
|
|
return torch.cat(y, -1), None # augmented inference, train
|
|
|
|
@staticmethod
|
|
def _descale_pred(p, flips, scale, img_size, dim=1):
|
|
"""
|
|
De-scale predictions following augmented inference (inverse operation).
|
|
|
|
Args:
|
|
p (torch.Tensor): Predictions tensor.
|
|
flips (int): Flip type (0=none, 2=ud, 3=lr).
|
|
scale (float): Scale factor.
|
|
img_size (tuple): Original image size (height, width).
|
|
dim (int): Dimension to split at.
|
|
|
|
Returns:
|
|
(torch.Tensor): De-scaled predictions.
|
|
"""
|
|
p[:, :4] /= scale # de-scale
|
|
x, y, wh, cls = p.split((1, 1, 2, p.shape[dim] - 4), dim)
|
|
if flips == 2:
|
|
y = img_size[0] - y # de-flip ud
|
|
elif flips == 3:
|
|
x = img_size[1] - x # de-flip lr
|
|
return torch.cat((x, y, wh, cls), dim)
|
|
|
|
def _clip_augmented(self, y):
|
|
"""
|
|
Clip YOLO augmented inference tails.
|
|
|
|
Args:
|
|
y (List[torch.Tensor]): List of detection tensors.
|
|
|
|
Returns:
|
|
(List[torch.Tensor]): Clipped detection tensors.
|
|
"""
|
|
nl = self.model[-1].nl # number of detection layers (P3-P5)
|
|
g = sum(4**x for x in range(nl)) # grid points
|
|
e = 1 # exclude layer count
|
|
i = (y[0].shape[-1] // g) * sum(4**x for x in range(e)) # indices
|
|
y[0] = y[0][..., :-i] # large
|
|
i = (y[-1].shape[-1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices
|
|
y[-1] = y[-1][..., i:] # small
|
|
return y
|
|
|
|
def init_criterion(self):
|
|
"""Initialize the loss criterion for the DetectionModel."""
|
|
return E2EDetectLoss(self) if getattr(self, "end2end", False) else v8DetectionLoss(self)
|
|
|
|
|
|
class OBBModel(DetectionModel):
|
|
"""
|
|
YOLO Oriented Bounding Box (OBB) model.
|
|
|
|
This class extends DetectionModel to handle oriented bounding box detection tasks, providing specialized
|
|
loss computation for rotated object detection.
|
|
|
|
Methods:
|
|
__init__: Initialize YOLO OBB model.
|
|
init_criterion: Initialize the loss criterion for OBB detection.
|
|
|
|
Examples:
|
|
Initialize an OBB model
|
|
>>> model = OBBModel("yolo11n-obb.yaml", ch=3, nc=80)
|
|
>>> results = model.predict(image_tensor)
|
|
"""
|
|
|
|
def __init__(self, cfg="yolo11n-obb.yaml", ch=3, nc=None, verbose=True):
|
|
"""
|
|
Initialize YOLO OBB model with given config and parameters.
|
|
|
|
Args:
|
|
cfg (str | dict): Model configuration file path or dictionary.
|
|
ch (int): Number of input channels.
|
|
nc (int, optional): Number of classes.
|
|
verbose (bool): Whether to display model information.
|
|
"""
|
|
super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
|
|
|
|
def init_criterion(self):
|
|
"""Initialize the loss criterion for the model."""
|
|
return v8OBBLoss(self)
|
|
|
|
|
|
class SegmentationModel(DetectionModel):
|
|
"""
|
|
YOLO segmentation model.
|
|
|
|
This class extends DetectionModel to handle instance segmentation tasks, providing specialized
|
|
loss computation for pixel-level object detection and segmentation.
|
|
|
|
Methods:
|
|
__init__: Initialize YOLO segmentation model.
|
|
init_criterion: Initialize the loss criterion for segmentation.
|
|
|
|
Examples:
|
|
Initialize a segmentation model
|
|
>>> model = SegmentationModel("yolo11n-seg.yaml", ch=3, nc=80)
|
|
>>> results = model.predict(image_tensor)
|
|
"""
|
|
|
|
def __init__(self, cfg="yolo11n-seg.yaml", ch=3, nc=None, verbose=True):
|
|
"""
|
|
Initialize Ultralytics YOLO segmentation model with given config and parameters.
|
|
|
|
Args:
|
|
cfg (str | dict): Model configuration file path or dictionary.
|
|
ch (int): Number of input channels.
|
|
nc (int, optional): Number of classes.
|
|
verbose (bool): Whether to display model information.
|
|
"""
|
|
super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
|
|
|
|
def init_criterion(self):
|
|
"""Initialize the loss criterion for the SegmentationModel."""
|
|
return v8SegmentationLoss(self)
|
|
|
|
|
|
class PoseModel(DetectionModel):
|
|
"""
|
|
YOLO pose model.
|
|
|
|
This class extends DetectionModel to handle human pose estimation tasks, providing specialized
|
|
loss computation for keypoint detection and pose estimation.
|
|
|
|
Attributes:
|
|
kpt_shape (tuple): Shape of keypoints data (num_keypoints, num_dimensions).
|
|
|
|
Methods:
|
|
__init__: Initialize YOLO pose model.
|
|
init_criterion: Initialize the loss criterion for pose estimation.
|
|
|
|
Examples:
|
|
Initialize a pose model
|
|
>>> model = PoseModel("yolo11n-pose.yaml", ch=3, nc=1, data_kpt_shape=(17, 3))
|
|
>>> results = model.predict(image_tensor)
|
|
"""
|
|
|
|
def __init__(self, cfg="yolo11n-pose.yaml", ch=3, nc=None, data_kpt_shape=(None, None), verbose=True):
|
|
"""
|
|
Initialize Ultralytics YOLO Pose model.
|
|
|
|
Args:
|
|
cfg (str | dict): Model configuration file path or dictionary.
|
|
ch (int): Number of input channels.
|
|
nc (int, optional): Number of classes.
|
|
data_kpt_shape (tuple): Shape of keypoints data.
|
|
verbose (bool): Whether to display model information.
|
|
"""
|
|
if not isinstance(cfg, dict):
|
|
cfg = yaml_model_load(cfg) # load model YAML
|
|
if any(data_kpt_shape) and list(data_kpt_shape) != list(cfg["kpt_shape"]):
|
|
LOGGER.info(f"Overriding model.yaml kpt_shape={cfg['kpt_shape']} with kpt_shape={data_kpt_shape}")
|
|
cfg["kpt_shape"] = data_kpt_shape
|
|
super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
|
|
|
|
def init_criterion(self):
|
|
"""Initialize the loss criterion for the PoseModel."""
|
|
return v8PoseLoss(self)
|
|
|
|
|
|
class ClassificationModel(BaseModel):
|
|
"""
|
|
YOLO classification model.
|
|
|
|
This class implements the YOLO classification architecture for image classification tasks,
|
|
providing model initialization, configuration, and output reshaping capabilities.
|
|
|
|
Attributes:
|
|
yaml (dict): Model configuration dictionary.
|
|
model (torch.nn.Sequential): The neural network model.
|
|
stride (torch.Tensor): Model stride values.
|
|
names (dict): Class names dictionary.
|
|
|
|
Methods:
|
|
__init__: Initialize ClassificationModel.
|
|
_from_yaml: Set model configurations and define architecture.
|
|
reshape_outputs: Update model to specified class count.
|
|
init_criterion: Initialize the loss criterion.
|
|
|
|
Examples:
|
|
Initialize a classification model
|
|
>>> model = ClassificationModel("yolo11n-cls.yaml", ch=3, nc=1000)
|
|
>>> results = model.predict(image_tensor)
|
|
"""
|
|
|
|
def __init__(self, cfg="yolo11n-cls.yaml", ch=3, nc=None, verbose=True):
|
|
"""
|
|
Initialize ClassificationModel with YAML, channels, number of classes, verbose flag.
|
|
|
|
Args:
|
|
cfg (str | dict): Model configuration file path or dictionary.
|
|
ch (int): Number of input channels.
|
|
nc (int, optional): Number of classes.
|
|
verbose (bool): Whether to display model information.
|
|
"""
|
|
super().__init__()
|
|
self._from_yaml(cfg, ch, nc, verbose)
|
|
|
|
def _from_yaml(self, cfg, ch, nc, verbose):
|
|
"""
|
|
Set Ultralytics YOLO model configurations and define the model architecture.
|
|
|
|
Args:
|
|
cfg (str | dict): Model configuration file path or dictionary.
|
|
ch (int): Number of input channels.
|
|
nc (int, optional): Number of classes.
|
|
verbose (bool): Whether to display model information.
|
|
"""
|
|
self.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg) # cfg dict
|
|
|
|
# Define model
|
|
ch = self.yaml["channels"] = self.yaml.get("channels", ch) # input channels
|
|
if nc and nc != self.yaml["nc"]:
|
|
LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
|
|
self.yaml["nc"] = nc # override YAML value
|
|
elif not nc and not self.yaml.get("nc", None):
|
|
raise ValueError("nc not specified. Must specify nc in model.yaml or function arguments.")
|
|
self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose) # model, savelist
|
|
self.stride = torch.Tensor([1]) # no stride constraints
|
|
self.names = {i: f"{i}" for i in range(self.yaml["nc"])} # default names dict
|
|
self.info()
|
|
|
|
@staticmethod
|
|
def reshape_outputs(model, nc):
|
|
"""
|
|
Update a TorchVision classification model to class count 'n' if required.
|
|
|
|
Args:
|
|
model (torch.nn.Module): Model to update.
|
|
nc (int): New number of classes.
|
|
"""
|
|
name, m = list((model.model if hasattr(model, "model") else model).named_children())[-1] # last module
|
|
if isinstance(m, Classify): # YOLO Classify() head
|
|
if m.linear.out_features != nc:
|
|
m.linear = torch.nn.Linear(m.linear.in_features, nc)
|
|
elif isinstance(m, torch.nn.Linear): # ResNet, EfficientNet
|
|
if m.out_features != nc:
|
|
setattr(model, name, torch.nn.Linear(m.in_features, nc))
|
|
elif isinstance(m, torch.nn.Sequential):
|
|
types = [type(x) for x in m]
|
|
if torch.nn.Linear in types:
|
|
i = len(types) - 1 - types[::-1].index(torch.nn.Linear) # last torch.nn.Linear index
|
|
if m[i].out_features != nc:
|
|
m[i] = torch.nn.Linear(m[i].in_features, nc)
|
|
elif torch.nn.Conv2d in types:
|
|
i = len(types) - 1 - types[::-1].index(torch.nn.Conv2d) # last torch.nn.Conv2d index
|
|
if m[i].out_channels != nc:
|
|
m[i] = torch.nn.Conv2d(
|
|
m[i].in_channels, nc, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None
|
|
)
|
|
|
|
def init_criterion(self):
|
|
"""Initialize the loss criterion for the ClassificationModel."""
|
|
return v8ClassificationLoss()
|
|
|
|
|
|
class RTDETRDetectionModel(DetectionModel):
|
|
"""
|
|
RTDETR (Real-time DEtection and Tracking using Transformers) Detection Model class.
|
|
|
|
This class is responsible for constructing the RTDETR architecture, defining loss functions, and facilitating both
|
|
the training and inference processes. RTDETR is an object detection and tracking model that extends from the
|
|
DetectionModel base class.
|
|
|
|
Attributes:
|
|
nc (int): Number of classes for detection.
|
|
criterion (RTDETRDetectionLoss): Loss function for training.
|
|
|
|
Methods:
|
|
__init__: Initialize the RTDETRDetectionModel.
|
|
init_criterion: Initialize the loss criterion.
|
|
loss: Compute loss for training.
|
|
predict: Perform forward pass through the model.
|
|
|
|
Examples:
|
|
Initialize an RTDETR model
|
|
>>> model = RTDETRDetectionModel("rtdetr-l.yaml", ch=3, nc=80)
|
|
>>> results = model.predict(image_tensor)
|
|
"""
|
|
|
|
def __init__(self, cfg="rtdetr-l.yaml", ch=3, nc=None, verbose=True):
|
|
"""
|
|
Initialize the RTDETRDetectionModel.
|
|
|
|
Args:
|
|
cfg (str | dict): Configuration file name or path.
|
|
ch (int): Number of input channels.
|
|
nc (int, optional): Number of classes.
|
|
verbose (bool): Print additional information during initialization.
|
|
"""
|
|
super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
|
|
|
|
def init_criterion(self):
|
|
"""Initialize the loss criterion for the RTDETRDetectionModel."""
|
|
from ultralytics.models.utils.loss import RTDETRDetectionLoss
|
|
|
|
return RTDETRDetectionLoss(nc=self.nc, use_vfl=True)
|
|
|
|
def loss(self, batch, preds=None):
|
|
"""
|
|
Compute the loss for the given batch of data.
|
|
|
|
Args:
|
|
batch (dict): Dictionary containing image and label data.
|
|
preds (torch.Tensor, optional): Precomputed model predictions.
|
|
|
|
Returns:
|
|
loss_sum (torch.Tensor): Total loss value.
|
|
loss_items (torch.Tensor): Main three losses in a tensor.
|
|
"""
|
|
if not hasattr(self, "criterion"):
|
|
self.criterion = self.init_criterion()
|
|
|
|
img = batch["img"]
|
|
# NOTE: preprocess gt_bbox and gt_labels to list.
|
|
bs = len(img)
|
|
batch_idx = batch["batch_idx"]
|
|
gt_groups = [(batch_idx == i).sum().item() for i in range(bs)]
|
|
targets = {
|
|
"cls": batch["cls"].to(img.device, dtype=torch.long).view(-1),
|
|
"bboxes": batch["bboxes"].to(device=img.device),
|
|
"batch_idx": batch_idx.to(img.device, dtype=torch.long).view(-1),
|
|
"gt_groups": gt_groups,
|
|
}
|
|
|
|
preds = self.predict(img, batch=targets) if preds is None else preds
|
|
dec_bboxes, dec_scores, enc_bboxes, enc_scores, dn_meta = preds if self.training else preds[1]
|
|
if dn_meta is None:
|
|
dn_bboxes, dn_scores = None, None
|
|
else:
|
|
dn_bboxes, dec_bboxes = torch.split(dec_bboxes, dn_meta["dn_num_split"], dim=2)
|
|
dn_scores, dec_scores = torch.split(dec_scores, dn_meta["dn_num_split"], dim=2)
|
|
|
|
dec_bboxes = torch.cat([enc_bboxes.unsqueeze(0), dec_bboxes]) # (7, bs, 300, 4)
|
|
dec_scores = torch.cat([enc_scores.unsqueeze(0), dec_scores])
|
|
|
|
loss = self.criterion(
|
|
(dec_bboxes, dec_scores), targets, dn_bboxes=dn_bboxes, dn_scores=dn_scores, dn_meta=dn_meta
|
|
)
|
|
# NOTE: There are like 12 losses in RTDETR, backward with all losses but only show the main three losses.
|
|
return sum(loss.values()), torch.as_tensor(
|
|
[loss[k].detach() for k in ["loss_giou", "loss_class", "loss_bbox"]], device=img.device
|
|
)
|
|
|
|
def predict(self, x, profile=False, visualize=False, batch=None, augment=False, embed=None):
|
|
"""
|
|
Perform a forward pass through the model.
|
|
|
|
Args:
|
|
x (torch.Tensor): The input tensor.
|
|
profile (bool): If True, profile the computation time for each layer.
|
|
visualize (bool): If True, save feature maps for visualization.
|
|
batch (dict, optional): Ground truth data for evaluation.
|
|
augment (bool): If True, perform data augmentation during inference.
|
|
embed (list, optional): A list of feature vectors/embeddings to return.
|
|
|
|
Returns:
|
|
(torch.Tensor): Model's output tensor.
|
|
"""
|
|
y, dt, embeddings = [], [], [] # outputs
|
|
embed = frozenset(embed) if embed is not None else {-1}
|
|
max_idx = max(embed)
|
|
for m in self.model[:-1]: # except the head part
|
|
if m.f != -1: # if not from previous layer
|
|
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
|
|
if profile:
|
|
self._profile_one_layer(m, x, dt)
|
|
x = m(x) # run
|
|
y.append(x if m.i in self.save else None) # save output
|
|
if visualize:
|
|
feature_visualization(x, m.type, m.i, save_dir=visualize)
|
|
if m.i in embed:
|
|
embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
|
|
if m.i == max_idx:
|
|
return torch.unbind(torch.cat(embeddings, 1), dim=0)
|
|
head = self.model[-1]
|
|
x = head([y[j] for j in head.f], batch) # head inference
|
|
return x
|
|
|
|
|
|
class WorldModel(DetectionModel):
|
|
"""
|
|
YOLOv8 World Model.
|
|
|
|
This class implements the YOLOv8 World model for open-vocabulary object detection, supporting text-based
|
|
class specification and CLIP model integration for zero-shot detection capabilities.
|
|
|
|
Attributes:
|
|
txt_feats (torch.Tensor): Text feature embeddings for classes.
|
|
clip_model (torch.nn.Module): CLIP model for text encoding.
|
|
|
|
Methods:
|
|
__init__: Initialize YOLOv8 world model.
|
|
set_classes: Set classes for offline inference.
|
|
get_text_pe: Get text positional embeddings.
|
|
predict: Perform forward pass with text features.
|
|
loss: Compute loss with text features.
|
|
|
|
Examples:
|
|
Initialize a world model
|
|
>>> model = WorldModel("yolov8s-world.yaml", ch=3, nc=80)
|
|
>>> model.set_classes(["person", "car", "bicycle"])
|
|
>>> results = model.predict(image_tensor)
|
|
"""
|
|
|
|
def __init__(self, cfg="yolov8s-world.yaml", ch=3, nc=None, verbose=True):
|
|
"""
|
|
Initialize YOLOv8 world model with given config and parameters.
|
|
|
|
Args:
|
|
cfg (str | dict): Model configuration file path or dictionary.
|
|
ch (int): Number of input channels.
|
|
nc (int, optional): Number of classes.
|
|
verbose (bool): Whether to display model information.
|
|
"""
|
|
self.txt_feats = torch.randn(1, nc or 80, 512) # features placeholder
|
|
self.clip_model = None # CLIP model placeholder
|
|
super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
|
|
|
|
def set_classes(self, text, batch=80, cache_clip_model=True):
|
|
"""
|
|
Set classes in advance so that model could do offline-inference without clip model.
|
|
|
|
Args:
|
|
text (List[str]): List of class names.
|
|
batch (int): Batch size for processing text tokens.
|
|
cache_clip_model (bool): Whether to cache the CLIP model.
|
|
"""
|
|
self.txt_feats = self.get_text_pe(text, batch=batch, cache_clip_model=cache_clip_model)
|
|
self.model[-1].nc = len(text)
|
|
|
|
def get_text_pe(self, text, batch=80, cache_clip_model=True):
|
|
"""
|
|
Set classes in advance so that model could do offline-inference without clip model.
|
|
|
|
Args:
|
|
text (List[str]): List of class names.
|
|
batch (int): Batch size for processing text tokens.
|
|
cache_clip_model (bool): Whether to cache the CLIP model.
|
|
|
|
Returns:
|
|
(torch.Tensor): Text positional embeddings.
|
|
"""
|
|
from ultralytics.nn.text_model import build_text_model
|
|
|
|
device = next(self.model.parameters()).device
|
|
if not getattr(self, "clip_model", None) and cache_clip_model:
|
|
# For backwards compatibility of models lacking clip_model attribute
|
|
self.clip_model = build_text_model("clip:ViT-B/32", device=device)
|
|
model = self.clip_model if cache_clip_model else build_text_model("clip:ViT-B/32", device=device)
|
|
text_token = model.tokenize(text)
|
|
txt_feats = [model.encode_text(token).detach() for token in text_token.split(batch)]
|
|
txt_feats = txt_feats[0] if len(txt_feats) == 1 else torch.cat(txt_feats, dim=0)
|
|
return txt_feats.reshape(-1, len(text), txt_feats.shape[-1])
|
|
|
|
def predict(self, x, profile=False, visualize=False, txt_feats=None, augment=False, embed=None):
|
|
"""
|
|
Perform a forward pass through the model.
|
|
|
|
Args:
|
|
x (torch.Tensor): The input tensor.
|
|
profile (bool): If True, profile the computation time for each layer.
|
|
visualize (bool): If True, save feature maps for visualization.
|
|
txt_feats (torch.Tensor, optional): The text features, use it if it's given.
|
|
augment (bool): If True, perform data augmentation during inference.
|
|
embed (list, optional): A list of feature vectors/embeddings to return.
|
|
|
|
Returns:
|
|
(torch.Tensor): Model's output tensor.
|
|
"""
|
|
txt_feats = (self.txt_feats if txt_feats is None else txt_feats).to(device=x.device, dtype=x.dtype)
|
|
if len(txt_feats) != len(x) or self.model[-1].export:
|
|
txt_feats = txt_feats.expand(x.shape[0], -1, -1)
|
|
ori_txt_feats = txt_feats.clone()
|
|
y, dt, embeddings = [], [], [] # outputs
|
|
embed = frozenset(embed) if embed is not None else {-1}
|
|
max_idx = max(embed)
|
|
for m in self.model: # except the head part
|
|
if m.f != -1: # if not from previous layer
|
|
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
|
|
if profile:
|
|
self._profile_one_layer(m, x, dt)
|
|
if isinstance(m, C2fAttn):
|
|
x = m(x, txt_feats)
|
|
elif isinstance(m, WorldDetect):
|
|
x = m(x, ori_txt_feats)
|
|
elif isinstance(m, ImagePoolingAttn):
|
|
txt_feats = m(x, txt_feats)
|
|
else:
|
|
x = m(x) # run
|
|
|
|
y.append(x if m.i in self.save else None) # save output
|
|
if visualize:
|
|
feature_visualization(x, m.type, m.i, save_dir=visualize)
|
|
if m.i in embed:
|
|
embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
|
|
if m.i == max_idx:
|
|
return torch.unbind(torch.cat(embeddings, 1), dim=0)
|
|
return x
|
|
|
|
def loss(self, batch, preds=None):
|
|
"""
|
|
Compute loss.
|
|
|
|
Args:
|
|
batch (dict): Batch to compute loss on.
|
|
preds (torch.Tensor | List[torch.Tensor], optional): Predictions.
|
|
"""
|
|
if not hasattr(self, "criterion"):
|
|
self.criterion = self.init_criterion()
|
|
|
|
if preds is None:
|
|
preds = self.forward(batch["img"], txt_feats=batch["txt_feats"])
|
|
return self.criterion(preds, batch)
|
|
|
|
|
|
class YOLOEModel(DetectionModel):
|
|
"""
|
|
YOLOE detection model.
|
|
|
|
This class implements the YOLOE architecture for efficient object detection with text and visual prompts,
|
|
supporting both prompt-based and prompt-free inference modes.
|
|
|
|
Attributes:
|
|
pe (torch.Tensor): Prompt embeddings for classes.
|
|
clip_model (torch.nn.Module): CLIP model for text encoding.
|
|
|
|
Methods:
|
|
__init__: Initialize YOLOE model.
|
|
get_text_pe: Get text positional embeddings.
|
|
get_visual_pe: Get visual embeddings.
|
|
set_vocab: Set vocabulary for prompt-free model.
|
|
get_vocab: Get fused vocabulary layer.
|
|
set_classes: Set classes for offline inference.
|
|
get_cls_pe: Get class positional embeddings.
|
|
predict: Perform forward pass with prompts.
|
|
loss: Compute loss with prompts.
|
|
|
|
Examples:
|
|
Initialize a YOLOE model
|
|
>>> model = YOLOEModel("yoloe-v8s.yaml", ch=3, nc=80)
|
|
>>> results = model.predict(image_tensor, tpe=text_embeddings)
|
|
"""
|
|
|
|
def __init__(self, cfg="yoloe-v8s.yaml", ch=3, nc=None, verbose=True):
|
|
"""
|
|
Initialize YOLOE model with given config and parameters.
|
|
|
|
Args:
|
|
cfg (str | dict): Model configuration file path or dictionary.
|
|
ch (int): Number of input channels.
|
|
nc (int, optional): Number of classes.
|
|
verbose (bool): Whether to display model information.
|
|
"""
|
|
super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
|
|
|
|
@smart_inference_mode()
|
|
def get_text_pe(self, text, batch=80, cache_clip_model=False, without_reprta=False):
|
|
"""
|
|
Set classes in advance so that model could do offline-inference without clip model.
|
|
|
|
Args:
|
|
text (List[str]): List of class names.
|
|
batch (int): Batch size for processing text tokens.
|
|
cache_clip_model (bool): Whether to cache the CLIP model.
|
|
without_reprta (bool): Whether to return text embeddings cooperated with reprta module.
|
|
|
|
Returns:
|
|
(torch.Tensor): Text positional embeddings.
|
|
"""
|
|
from ultralytics.nn.text_model import build_text_model
|
|
|
|
device = next(self.model.parameters()).device
|
|
if not getattr(self, "clip_model", None) and cache_clip_model:
|
|
# For backwards compatibility of models lacking clip_model attribute
|
|
self.clip_model = build_text_model("mobileclip:blt", device=device)
|
|
|
|
model = self.clip_model if cache_clip_model else build_text_model("mobileclip:blt", device=device)
|
|
text_token = model.tokenize(text)
|
|
txt_feats = [model.encode_text(token).detach() for token in text_token.split(batch)]
|
|
txt_feats = txt_feats[0] if len(txt_feats) == 1 else torch.cat(txt_feats, dim=0)
|
|
txt_feats = txt_feats.reshape(-1, len(text), txt_feats.shape[-1])
|
|
if without_reprta:
|
|
return txt_feats
|
|
|
|
assert not self.training
|
|
head = self.model[-1]
|
|
assert isinstance(head, YOLOEDetect)
|
|
return head.get_tpe(txt_feats) # run auxiliary text head
|
|
|
|
@smart_inference_mode()
|
|
def get_visual_pe(self, img, visual):
|
|
"""
|
|
Get visual embeddings.
|
|
|
|
Args:
|
|
img (torch.Tensor): Input image tensor.
|
|
visual (torch.Tensor): Visual features.
|
|
|
|
Returns:
|
|
(torch.Tensor): Visual positional embeddings.
|
|
"""
|
|
return self(img, vpe=visual, return_vpe=True)
|
|
|
|
def set_vocab(self, vocab, names):
|
|
"""
|
|
Set vocabulary for the prompt-free model.
|
|
|
|
Args:
|
|
vocab (nn.ModuleList): List of vocabulary items.
|
|
names (List[str]): List of class names.
|
|
"""
|
|
assert not self.training
|
|
head = self.model[-1]
|
|
assert isinstance(head, YOLOEDetect)
|
|
|
|
# Cache anchors for head
|
|
device = next(self.parameters()).device
|
|
self(torch.empty(1, 3, self.args["imgsz"], self.args["imgsz"]).to(device)) # warmup
|
|
|
|
# re-parameterization for prompt-free model
|
|
self.model[-1].lrpc = nn.ModuleList(
|
|
LRPCHead(cls, pf[-1], loc[-1], enabled=i != 2)
|
|
for i, (cls, pf, loc) in enumerate(zip(vocab, head.cv3, head.cv2))
|
|
)
|
|
for loc_head, cls_head in zip(head.cv2, head.cv3):
|
|
assert isinstance(loc_head, nn.Sequential)
|
|
assert isinstance(cls_head, nn.Sequential)
|
|
del loc_head[-1]
|
|
del cls_head[-1]
|
|
self.model[-1].nc = len(names)
|
|
self.names = check_class_names(names)
|
|
|
|
def get_vocab(self, names):
|
|
"""
|
|
Get fused vocabulary layer from the model.
|
|
|
|
Args:
|
|
names (list): List of class names.
|
|
|
|
Returns:
|
|
(nn.ModuleList): List of vocabulary modules.
|
|
"""
|
|
assert not self.training
|
|
head = self.model[-1]
|
|
assert isinstance(head, YOLOEDetect)
|
|
assert not head.is_fused
|
|
|
|
tpe = self.get_text_pe(names)
|
|
self.set_classes(names, tpe)
|
|
device = next(self.model.parameters()).device
|
|
head.fuse(self.pe.to(device)) # fuse prompt embeddings to classify head
|
|
|
|
vocab = nn.ModuleList()
|
|
for cls_head in head.cv3:
|
|
assert isinstance(cls_head, nn.Sequential)
|
|
vocab.append(cls_head[-1])
|
|
return vocab
|
|
|
|
def set_classes(self, names, embeddings):
|
|
"""
|
|
Set classes in advance so that model could do offline-inference without clip model.
|
|
|
|
Args:
|
|
names (List[str]): List of class names.
|
|
embeddings (torch.Tensor): Embeddings tensor.
|
|
"""
|
|
assert not hasattr(self.model[-1], "lrpc"), (
|
|
"Prompt-free model does not support setting classes. Please try with Text/Visual prompt models."
|
|
)
|
|
assert embeddings.ndim == 3
|
|
self.pe = embeddings
|
|
self.model[-1].nc = len(names)
|
|
self.names = check_class_names(names)
|
|
|
|
def get_cls_pe(self, tpe, vpe):
|
|
"""
|
|
Get class positional embeddings.
|
|
|
|
Args:
|
|
tpe (torch.Tensor, optional): Text positional embeddings.
|
|
vpe (torch.Tensor, optional): Visual positional embeddings.
|
|
|
|
Returns:
|
|
(torch.Tensor): Class positional embeddings.
|
|
"""
|
|
all_pe = []
|
|
if tpe is not None:
|
|
assert tpe.ndim == 3
|
|
all_pe.append(tpe)
|
|
if vpe is not None:
|
|
assert vpe.ndim == 3
|
|
all_pe.append(vpe)
|
|
if not all_pe:
|
|
all_pe.append(getattr(self, "pe", torch.zeros(1, 80, 512)))
|
|
return torch.cat(all_pe, dim=1)
|
|
|
|
def predict(
|
|
self, x, profile=False, visualize=False, tpe=None, augment=False, embed=None, vpe=None, return_vpe=False
|
|
):
|
|
"""
|
|
Perform a forward pass through the model.
|
|
|
|
Args:
|
|
x (torch.Tensor): The input tensor.
|
|
profile (bool): If True, profile the computation time for each layer.
|
|
visualize (bool): If True, save feature maps for visualization.
|
|
tpe (torch.Tensor, optional): Text positional embeddings.
|
|
augment (bool): If True, perform data augmentation during inference.
|
|
embed (list, optional): A list of feature vectors/embeddings to return.
|
|
vpe (torch.Tensor, optional): Visual positional embeddings.
|
|
return_vpe (bool): If True, return visual positional embeddings.
|
|
|
|
Returns:
|
|
(torch.Tensor): Model's output tensor.
|
|
"""
|
|
y, dt, embeddings = [], [], [] # outputs
|
|
b = x.shape[0]
|
|
embed = frozenset(embed) if embed is not None else {-1}
|
|
max_idx = max(embed)
|
|
for m in self.model: # except the head part
|
|
if m.f != -1: # if not from previous layer
|
|
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
|
|
if profile:
|
|
self._profile_one_layer(m, x, dt)
|
|
if isinstance(m, YOLOEDetect):
|
|
vpe = m.get_vpe(x, vpe) if vpe is not None else None
|
|
if return_vpe:
|
|
assert vpe is not None
|
|
assert not self.training
|
|
return vpe
|
|
cls_pe = self.get_cls_pe(m.get_tpe(tpe), vpe).to(device=x[0].device, dtype=x[0].dtype)
|
|
if cls_pe.shape[0] != b or m.export:
|
|
cls_pe = cls_pe.expand(b, -1, -1)
|
|
x = m(x, cls_pe)
|
|
else:
|
|
x = m(x) # run
|
|
|
|
y.append(x if m.i in self.save else None) # save output
|
|
if visualize:
|
|
feature_visualization(x, m.type, m.i, save_dir=visualize)
|
|
if m.i in embed:
|
|
embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
|
|
if m.i == max_idx:
|
|
return torch.unbind(torch.cat(embeddings, 1), dim=0)
|
|
return x
|
|
|
|
def loss(self, batch, preds=None):
|
|
"""
|
|
Compute loss.
|
|
|
|
Args:
|
|
batch (dict): Batch to compute loss on.
|
|
preds (torch.Tensor | List[torch.Tensor], optional): Predictions.
|
|
"""
|
|
if not hasattr(self, "criterion"):
|
|
from ultralytics.utils.loss import TVPDetectLoss
|
|
|
|
visual_prompt = batch.get("visuals", None) is not None # TODO
|
|
self.criterion = TVPDetectLoss(self) if visual_prompt else self.init_criterion()
|
|
|
|
if preds is None:
|
|
preds = self.forward(batch["img"], tpe=batch.get("txt_feats", None), vpe=batch.get("visuals", None))
|
|
return self.criterion(preds, batch)
|
|
|
|
|
|
class YOLOESegModel(YOLOEModel, SegmentationModel):
|
|
"""
|
|
YOLOE segmentation model.
|
|
|
|
This class extends YOLOEModel to handle instance segmentation tasks with text and visual prompts,
|
|
providing specialized loss computation for pixel-level object detection and segmentation.
|
|
|
|
Methods:
|
|
__init__: Initialize YOLOE segmentation model.
|
|
loss: Compute loss with prompts for segmentation.
|
|
|
|
Examples:
|
|
Initialize a YOLOE segmentation model
|
|
>>> model = YOLOESegModel("yoloe-v8s-seg.yaml", ch=3, nc=80)
|
|
>>> results = model.predict(image_tensor, tpe=text_embeddings)
|
|
"""
|
|
|
|
def __init__(self, cfg="yoloe-v8s-seg.yaml", ch=3, nc=None, verbose=True):
|
|
"""
|
|
Initialize YOLOE segmentation model with given config and parameters.
|
|
|
|
Args:
|
|
cfg (str | dict): Model configuration file path or dictionary.
|
|
ch (int): Number of input channels.
|
|
nc (int, optional): Number of classes.
|
|
verbose (bool): Whether to display model information.
|
|
"""
|
|
super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
|
|
|
|
def loss(self, batch, preds=None):
|
|
"""
|
|
Compute loss.
|
|
|
|
Args:
|
|
batch (dict): Batch to compute loss on.
|
|
preds (torch.Tensor | List[torch.Tensor], optional): Predictions.
|
|
"""
|
|
if not hasattr(self, "criterion"):
|
|
from ultralytics.utils.loss import TVPSegmentLoss
|
|
|
|
visual_prompt = batch.get("visuals", None) is not None # TODO
|
|
self.criterion = TVPSegmentLoss(self) if visual_prompt else self.init_criterion()
|
|
|
|
if preds is None:
|
|
preds = self.forward(batch["img"], tpe=batch.get("txt_feats", None), vpe=batch.get("visuals", None))
|
|
return self.criterion(preds, batch)
|
|
|
|
|
|
class Ensemble(torch.nn.ModuleList):
|
|
"""
|
|
Ensemble of models.
|
|
|
|
This class allows combining multiple YOLO models into an ensemble for improved performance through
|
|
model averaging or other ensemble techniques.
|
|
|
|
Methods:
|
|
__init__: Initialize an ensemble of models.
|
|
forward: Generate predictions from all models in the ensemble.
|
|
|
|
Examples:
|
|
Create an ensemble of models
|
|
>>> ensemble = Ensemble()
|
|
>>> ensemble.append(model1)
|
|
>>> ensemble.append(model2)
|
|
>>> results = ensemble(image_tensor)
|
|
"""
|
|
|
|
def __init__(self):
|
|
"""Initialize an ensemble of models."""
|
|
super().__init__()
|
|
|
|
def forward(self, x, augment=False, profile=False, visualize=False):
|
|
"""
|
|
Generate the YOLO network's final layer.
|
|
|
|
Args:
|
|
x (torch.Tensor): Input tensor.
|
|
augment (bool): Whether to augment the input.
|
|
profile (bool): Whether to profile the model.
|
|
visualize (bool): Whether to visualize the features.
|
|
|
|
Returns:
|
|
y (torch.Tensor): Concatenated predictions from all models.
|
|
train_out (None): Always None for ensemble inference.
|
|
"""
|
|
y = [module(x, augment, profile, visualize)[0] for module in self]
|
|
# y = torch.stack(y).max(0)[0] # max ensemble
|
|
# y = torch.stack(y).mean(0) # mean ensemble
|
|
y = torch.cat(y, 2) # nms ensemble, y shape(B, HW, C)
|
|
return y, None # inference, train output
|
|
|
|
|
|
# Functions ------------------------------------------------------------------------------------------------------------
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def temporary_modules(modules=None, attributes=None):
|
|
"""
|
|
Context manager for temporarily adding or modifying modules in Python's module cache (`sys.modules`).
|
|
|
|
This function can be used to change the module paths during runtime. It's useful when refactoring code,
|
|
where you've moved a module from one location to another, but you still want to support the old import
|
|
paths for backwards compatibility.
|
|
|
|
Args:
|
|
modules (dict, optional): A dictionary mapping old module paths to new module paths.
|
|
attributes (dict, optional): A dictionary mapping old module attributes to new module attributes.
|
|
|
|
Examples:
|
|
>>> with temporary_modules({"old.module": "new.module"}, {"old.module.attribute": "new.module.attribute"}):
|
|
>>> import old.module # this will now import new.module
|
|
>>> from old.module import attribute # this will now import new.module.attribute
|
|
|
|
Note:
|
|
The changes are only in effect inside the context manager and are undone once the context manager exits.
|
|
Be aware that directly manipulating `sys.modules` can lead to unpredictable results, especially in larger
|
|
applications or libraries. Use this function with caution.
|
|
"""
|
|
if modules is None:
|
|
modules = {}
|
|
if attributes is None:
|
|
attributes = {}
|
|
import sys
|
|
from importlib import import_module
|
|
|
|
try:
|
|
# Set attributes in sys.modules under their old name
|
|
for old, new in attributes.items():
|
|
old_module, old_attr = old.rsplit(".", 1)
|
|
new_module, new_attr = new.rsplit(".", 1)
|
|
setattr(import_module(old_module), old_attr, getattr(import_module(new_module), new_attr))
|
|
|
|
# Set modules in sys.modules under their old name
|
|
for old, new in modules.items():
|
|
sys.modules[old] = import_module(new)
|
|
|
|
yield
|
|
finally:
|
|
# Remove the temporary module paths
|
|
for old in modules:
|
|
if old in sys.modules:
|
|
del sys.modules[old]
|
|
|
|
|
|
class SafeClass:
|
|
"""A placeholder class to replace unknown classes during unpickling."""
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
"""Initialize SafeClass instance, ignoring all arguments."""
|
|
pass
|
|
|
|
def __call__(self, *args, **kwargs):
|
|
"""Run SafeClass instance, ignoring all arguments."""
|
|
pass
|
|
|
|
|
|
class SafeUnpickler(pickle.Unpickler):
|
|
"""Custom Unpickler that replaces unknown classes with SafeClass."""
|
|
|
|
def find_class(self, module, name):
|
|
"""
|
|
Attempt to find a class, returning SafeClass if not among safe modules.
|
|
|
|
Args:
|
|
module (str): Module name.
|
|
name (str): Class name.
|
|
|
|
Returns:
|
|
(type): Found class or SafeClass.
|
|
"""
|
|
safe_modules = (
|
|
"torch",
|
|
"collections",
|
|
"collections.abc",
|
|
"builtins",
|
|
"math",
|
|
"numpy",
|
|
# Add other modules considered safe
|
|
)
|
|
if module in safe_modules:
|
|
return super().find_class(module, name)
|
|
else:
|
|
return SafeClass
|
|
|
|
|
|
def torch_safe_load(weight, safe_only=False):
|
|
"""
|
|
Attempt to load a PyTorch model with the torch.load() function. If a ModuleNotFoundError is raised, it catches the
|
|
error, logs a warning message, and attempts to install the missing module via the check_requirements() function.
|
|
After installation, the function again attempts to load the model using torch.load().
|
|
|
|
Args:
|
|
weight (str): The file path of the PyTorch model.
|
|
safe_only (bool): If True, replace unknown classes with SafeClass during loading.
|
|
|
|
Returns:
|
|
ckpt (dict): The loaded model checkpoint.
|
|
file (str): The loaded filename.
|
|
|
|
Examples:
|
|
>>> from ultralytics.nn.tasks import torch_safe_load
|
|
>>> ckpt, file = torch_safe_load("path/to/best.pt", safe_only=True)
|
|
"""
|
|
from ultralytics.utils.downloads import attempt_download_asset
|
|
|
|
check_suffix(file=weight, suffix=".pt")
|
|
file = attempt_download_asset(weight) # search online if missing locally
|
|
try:
|
|
with temporary_modules(
|
|
modules={
|
|
"ultralytics.yolo.utils": "ultralytics.utils",
|
|
"ultralytics.yolo.v8": "ultralytics.models.yolo",
|
|
"ultralytics.yolo.data": "ultralytics.data",
|
|
},
|
|
attributes={
|
|
"ultralytics.nn.modules.block.Silence": "torch.nn.Identity", # YOLOv9e
|
|
"ultralytics.nn.tasks.YOLOv10DetectionModel": "ultralytics.nn.tasks.DetectionModel", # YOLOv10
|
|
"ultralytics.utils.loss.v10DetectLoss": "ultralytics.utils.loss.E2EDetectLoss", # YOLOv10
|
|
},
|
|
):
|
|
if safe_only:
|
|
# Load via custom pickle module
|
|
safe_pickle = types.ModuleType("safe_pickle")
|
|
safe_pickle.Unpickler = SafeUnpickler
|
|
safe_pickle.load = lambda file_obj: SafeUnpickler(file_obj).load()
|
|
with open(file, "rb") as f:
|
|
ckpt = torch.load(f, pickle_module=safe_pickle)
|
|
else:
|
|
ckpt = torch.load(file, map_location="cpu")
|
|
|
|
except ModuleNotFoundError as e: # e.name is missing module name
|
|
if e.name == "models":
|
|
raise TypeError(
|
|
emojis(
|
|
f"ERROR ❌️ {weight} appears to be an Ultralytics YOLOv5 model originally trained "
|
|
f"with https://github.com/ultralytics/yolov5.\nThis model is NOT forwards compatible with "
|
|
f"YOLOv8 at https://github.com/ultralytics/ultralytics."
|
|
f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
|
|
f"run a command with an official Ultralytics model, i.e. 'yolo predict model=yolo11n.pt'"
|
|
)
|
|
) from e
|
|
elif e.name == "numpy._core":
|
|
raise ModuleNotFoundError(
|
|
emojis(
|
|
f"ERROR ❌️ {weight} requires numpy>=1.26.1, however numpy=={__import__('numpy').__version__} is installed."
|
|
)
|
|
) from e
|
|
LOGGER.warning(
|
|
f"{weight} appears to require '{e.name}', which is not in Ultralytics requirements."
|
|
f"\nAutoInstall will run now for '{e.name}' but this feature will be removed in the future."
|
|
f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
|
|
f"run a command with an official Ultralytics model, i.e. 'yolo predict model=yolo11n.pt'"
|
|
)
|
|
check_requirements(e.name) # install missing module
|
|
ckpt = torch.load(file, map_location="cpu")
|
|
|
|
if not isinstance(ckpt, dict):
|
|
# File is likely a YOLO instance saved with i.e. torch.save(model, "saved_model.pt")
|
|
LOGGER.warning(
|
|
f"The file '{weight}' appears to be improperly saved or formatted. "
|
|
f"For optimal results, use model.save('filename.pt') to correctly save YOLO models."
|
|
)
|
|
ckpt = {"model": ckpt.model}
|
|
|
|
return ckpt, file
|
|
|
|
|
|
def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
|
|
"""
|
|
Load an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a.
|
|
|
|
Args:
|
|
weights (str | List[str]): Model weights path(s).
|
|
device (torch.device, optional): Device to load model to.
|
|
inplace (bool): Whether to do inplace operations.
|
|
fuse (bool): Whether to fuse model.
|
|
|
|
Returns:
|
|
(torch.nn.Module): Loaded model.
|
|
"""
|
|
ensemble = Ensemble()
|
|
for w in weights if isinstance(weights, list) else [weights]:
|
|
ckpt, w = torch_safe_load(w) # load ckpt
|
|
args = {**DEFAULT_CFG_DICT, **ckpt["train_args"]} if "train_args" in ckpt else None # combined args
|
|
model = (ckpt.get("ema") or ckpt["model"]).to(device).float() # FP32 model
|
|
|
|
# Model compatibility updates
|
|
model.args = args # attach args to model
|
|
model.pt_path = w # attach *.pt file path to model
|
|
model.task = getattr(model, "task", guess_model_task(model))
|
|
if not hasattr(model, "stride"):
|
|
model.stride = torch.tensor([32.0])
|
|
|
|
# Append
|
|
ensemble.append(model.fuse().eval() if fuse and hasattr(model, "fuse") else model.eval()) # model in eval mode
|
|
|
|
# Module updates
|
|
for m in ensemble.modules():
|
|
if hasattr(m, "inplace"):
|
|
m.inplace = inplace
|
|
elif isinstance(m, torch.nn.Upsample) and not hasattr(m, "recompute_scale_factor"):
|
|
m.recompute_scale_factor = None # torch 1.11.0 compatibility
|
|
|
|
# Return model
|
|
if len(ensemble) == 1:
|
|
return ensemble[-1]
|
|
|
|
# Return ensemble
|
|
LOGGER.info(f"Ensemble created with {weights}\n")
|
|
for k in "names", "nc", "yaml":
|
|
setattr(ensemble, k, getattr(ensemble[0], k))
|
|
ensemble.stride = ensemble[int(torch.argmax(torch.tensor([m.stride.max() for m in ensemble])))].stride
|
|
assert all(ensemble[0].nc == m.nc for m in ensemble), f"Models differ in class counts {[m.nc for m in ensemble]}"
|
|
return ensemble
|
|
|
|
|
|
def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False):
|
|
"""
|
|
Load a single model weights.
|
|
|
|
Args:
|
|
weight (str): Model weight path.
|
|
device (torch.device, optional): Device to load model to.
|
|
inplace (bool): Whether to do inplace operations.
|
|
fuse (bool): Whether to fuse model.
|
|
|
|
Returns:
|
|
model (torch.nn.Module): Loaded model.
|
|
ckpt (dict): Model checkpoint dictionary.
|
|
"""
|
|
ckpt, weight = torch_safe_load(weight) # load ckpt
|
|
args = {**DEFAULT_CFG_DICT, **(ckpt.get("train_args", {}))} # combine model and default args, preferring model args
|
|
model = (ckpt.get("ema") or ckpt["model"]).to(device).float() # FP32 model
|
|
|
|
# Model compatibility updates
|
|
model.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS} # attach args to model
|
|
model.pt_path = weight # attach *.pt file path to model
|
|
model.task = getattr(model, "task", guess_model_task(model))
|
|
if not hasattr(model, "stride"):
|
|
model.stride = torch.tensor([32.0])
|
|
|
|
model = model.fuse().eval() if fuse and hasattr(model, "fuse") else model.eval() # model in eval mode
|
|
|
|
# Module updates
|
|
for m in model.modules():
|
|
if hasattr(m, "inplace"):
|
|
m.inplace = inplace
|
|
elif isinstance(m, torch.nn.Upsample) and not hasattr(m, "recompute_scale_factor"):
|
|
m.recompute_scale_factor = None # torch 1.11.0 compatibility
|
|
|
|
# Return model and ckpt
|
|
return model, ckpt
|
|
|
|
|
|
def parse_model(d, ch, verbose=True):
|
|
"""
|
|
Parse a YOLO model.yaml dictionary into a PyTorch model.
|
|
|
|
Args:
|
|
d (dict): Model dictionary.
|
|
ch (int): Input channels.
|
|
verbose (bool): Whether to print model details.
|
|
|
|
Returns:
|
|
model (torch.nn.Sequential): PyTorch model.
|
|
save (list): Sorted list of output layers.
|
|
"""
|
|
import ast
|
|
|
|
# Args
|
|
legacy = True # backward compatibility for v3/v5/v8/v9 models
|
|
max_channels = float("inf")
|
|
nc, act, scales = (d.get(x) for x in ("nc", "activation", "scales"))
|
|
depth, width, kpt_shape = (d.get(x, 1.0) for x in ("depth_multiple", "width_multiple", "kpt_shape"))
|
|
if scales:
|
|
scale = d.get("scale")
|
|
if not scale:
|
|
scale = tuple(scales.keys())[0]
|
|
LOGGER.warning(f"no model scale passed. Assuming scale='{scale}'.")
|
|
depth, width, max_channels = scales[scale]
|
|
|
|
if act:
|
|
Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = torch.nn.SiLU()
|
|
if verbose:
|
|
LOGGER.info(f"{colorstr('activation:')} {act}") # print
|
|
|
|
if verbose:
|
|
LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10} {'module':<45}{'arguments':<30}")
|
|
ch = [ch]
|
|
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
|
|
base_modules = frozenset(
|
|
{
|
|
Classify,
|
|
Conv,
|
|
ConvTranspose,
|
|
GhostConv,
|
|
Bottleneck,
|
|
GhostBottleneck,
|
|
SPP,
|
|
SPPF,
|
|
C2fPSA,
|
|
C2PSA,
|
|
DWConv,
|
|
Focus,
|
|
BottleneckCSP,
|
|
C1,
|
|
C2,
|
|
C2f,
|
|
C3k2,
|
|
RepNCSPELAN4,
|
|
ELAN1,
|
|
ADown,
|
|
AConv,
|
|
SPPELAN,
|
|
C2fAttn,
|
|
C3,
|
|
C3TR,
|
|
C3Ghost,
|
|
torch.nn.ConvTranspose2d,
|
|
DWConvTranspose2d,
|
|
C3x,
|
|
RepC3,
|
|
PSA,
|
|
SCDown,
|
|
C2fCIB,
|
|
A2C2f,
|
|
}
|
|
)
|
|
repeat_modules = frozenset( # modules with 'repeat' arguments
|
|
{
|
|
BottleneckCSP,
|
|
C1,
|
|
C2,
|
|
C2f,
|
|
C3k2,
|
|
C2fAttn,
|
|
C3,
|
|
C3TR,
|
|
C3Ghost,
|
|
C3x,
|
|
RepC3,
|
|
C2fPSA,
|
|
C2fCIB,
|
|
C2PSA,
|
|
A2C2f,
|
|
}
|
|
)
|
|
for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args
|
|
m = (
|
|
getattr(torch.nn, m[3:])
|
|
if "nn." in m
|
|
else getattr(__import__("torchvision").ops, m[16:])
|
|
if "torchvision.ops." in m
|
|
else globals()[m]
|
|
) # get module
|
|
for j, a in enumerate(args):
|
|
if isinstance(a, str):
|
|
with contextlib.suppress(ValueError):
|
|
args[j] = locals()[a] if a in locals() else ast.literal_eval(a)
|
|
n = n_ = max(round(n * depth), 1) if n > 1 else n # depth gain
|
|
if m in base_modules:
|
|
c1, c2 = ch[f], args[0]
|
|
if c2 != nc: # if c2 not equal to number of classes (i.e. for Classify() output)
|
|
c2 = make_divisible(min(c2, max_channels) * width, 8)
|
|
if m is C2fAttn: # set 1) embed channels and 2) num heads
|
|
args[1] = make_divisible(min(args[1], max_channels // 2) * width, 8)
|
|
args[2] = int(max(round(min(args[2], max_channels // 2 // 32)) * width, 1) if args[2] > 1 else args[2])
|
|
|
|
args = [c1, c2, *args[1:]]
|
|
if m in repeat_modules:
|
|
args.insert(2, n) # number of repeats
|
|
n = 1
|
|
if m is C3k2: # for M/L/X sizes
|
|
legacy = False
|
|
if scale in "mlx":
|
|
args[3] = True
|
|
if m is A2C2f:
|
|
legacy = False
|
|
if scale in "lx": # for L/X sizes
|
|
args.extend((True, 1.2))
|
|
if m is C2fCIB:
|
|
legacy = False
|
|
elif m is AIFI:
|
|
args = [ch[f], *args]
|
|
elif m in frozenset({HGStem, HGBlock}):
|
|
c1, cm, c2 = ch[f], args[0], args[1]
|
|
args = [c1, cm, c2, *args[2:]]
|
|
if m is HGBlock:
|
|
args.insert(4, n) # number of repeats
|
|
n = 1
|
|
elif m is ResNetLayer:
|
|
c2 = args[1] if args[3] else args[1] * 4
|
|
elif m is torch.nn.BatchNorm2d:
|
|
args = [ch[f]]
|
|
elif m is Concat:
|
|
c2 = sum(ch[x] for x in f)
|
|
elif m in frozenset(
|
|
{Detect, WorldDetect, YOLOEDetect, Segment, YOLOESegment, Pose, OBB, ImagePoolingAttn, v10Detect}
|
|
):
|
|
args.append([ch[x] for x in f])
|
|
if m is Segment or m is YOLOESegment:
|
|
args[2] = make_divisible(min(args[2], max_channels) * width, 8)
|
|
if m in {Detect, YOLOEDetect, Segment, YOLOESegment, Pose, OBB}:
|
|
m.legacy = legacy
|
|
elif m is RTDETRDecoder: # special case, channels arg must be passed in index 1
|
|
args.insert(1, [ch[x] for x in f])
|
|
elif m is CBLinear:
|
|
c2 = args[0]
|
|
c1 = ch[f]
|
|
args = [c1, c2, *args[1:]]
|
|
elif m is CBFuse:
|
|
c2 = ch[f[-1]]
|
|
elif m in frozenset({TorchVision, Index}):
|
|
c2 = args[0]
|
|
c1 = ch[f]
|
|
args = [*args[1:]]
|
|
else:
|
|
c2 = ch[f]
|
|
|
|
m_ = torch.nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
|
|
t = str(m)[8:-2].replace("__main__.", "") # module type
|
|
m_.np = sum(x.numel() for x in m_.parameters()) # number params
|
|
m_.i, m_.f, m_.type = i, f, t # attach index, 'from' index, type
|
|
if verbose:
|
|
LOGGER.info(f"{i:>3}{str(f):>20}{n_:>3}{m_.np:10.0f} {t:<45}{str(args):<30}") # print
|
|
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
|
|
layers.append(m_)
|
|
if i == 0:
|
|
ch = []
|
|
ch.append(c2)
|
|
return torch.nn.Sequential(*layers), sorted(save)
|
|
|
|
|
|
def yaml_model_load(path):
|
|
"""
|
|
Load a YOLOv8 model from a YAML file.
|
|
|
|
Args:
|
|
path (str | Path): Path to the YAML file.
|
|
|
|
Returns:
|
|
(dict): Model dictionary.
|
|
"""
|
|
path = Path(path)
|
|
if path.stem in (f"yolov{d}{x}6" for x in "nsmlx" for d in (5, 8)):
|
|
new_stem = re.sub(r"(\d+)([nslmx])6(.+)?$", r"\1\2-p6\3", path.stem)
|
|
LOGGER.warning(f"Ultralytics YOLO P6 models now use -p6 suffix. Renaming {path.stem} to {new_stem}.")
|
|
path = path.with_name(new_stem + path.suffix)
|
|
|
|
unified_path = re.sub(r"(\d+)([nslmx])(.+)?$", r"\1\3", str(path)) # i.e. yolov8x.yaml -> yolov8.yaml
|
|
yaml_file = check_yaml(unified_path, hard=False) or check_yaml(path)
|
|
d = YAML.load(yaml_file) # model dict
|
|
d["scale"] = guess_model_scale(path)
|
|
d["yaml_file"] = str(path)
|
|
return d
|
|
|
|
|
|
def guess_model_scale(model_path):
|
|
"""
|
|
Extract the size character n, s, m, l, or x of the model's scale from the model path.
|
|
|
|
Args:
|
|
model_path (str | Path): The path to the YOLO model's YAML file.
|
|
|
|
Returns:
|
|
(str): The size character of the model's scale (n, s, m, l, or x).
|
|
"""
|
|
try:
|
|
return re.search(r"yolo(e-)?[v]?\d+([nslmx])", Path(model_path).stem).group(2) # noqa
|
|
except AttributeError:
|
|
return ""
|
|
|
|
|
|
def guess_model_task(model):
|
|
"""
|
|
Guess the task of a PyTorch model from its architecture or configuration.
|
|
|
|
Args:
|
|
model (torch.nn.Module | dict): PyTorch model or model configuration in YAML format.
|
|
|
|
Returns:
|
|
(str): Task of the model ('detect', 'segment', 'classify', 'pose', 'obb').
|
|
"""
|
|
|
|
def cfg2task(cfg):
|
|
"""Guess from YAML dictionary."""
|
|
m = cfg["head"][-1][-2].lower() # output module name
|
|
if m in {"classify", "classifier", "cls", "fc"}:
|
|
return "classify"
|
|
if "detect" in m:
|
|
return "detect"
|
|
if "segment" in m:
|
|
return "segment"
|
|
if m == "pose":
|
|
return "pose"
|
|
if m == "obb":
|
|
return "obb"
|
|
|
|
# Guess from model cfg
|
|
if isinstance(model, dict):
|
|
with contextlib.suppress(Exception):
|
|
return cfg2task(model)
|
|
# Guess from PyTorch model
|
|
if isinstance(model, torch.nn.Module): # PyTorch model
|
|
for x in "model.args", "model.model.args", "model.model.model.args":
|
|
with contextlib.suppress(Exception):
|
|
return eval(x)["task"]
|
|
for x in "model.yaml", "model.model.yaml", "model.model.model.yaml":
|
|
with contextlib.suppress(Exception):
|
|
return cfg2task(eval(x))
|
|
for m in model.modules():
|
|
if isinstance(m, (Segment, YOLOESegment)):
|
|
return "segment"
|
|
elif isinstance(m, Classify):
|
|
return "classify"
|
|
elif isinstance(m, Pose):
|
|
return "pose"
|
|
elif isinstance(m, OBB):
|
|
return "obb"
|
|
elif isinstance(m, (Detect, WorldDetect, YOLOEDetect, v10Detect)):
|
|
return "detect"
|
|
|
|
# Guess from model filename
|
|
if isinstance(model, (str, Path)):
|
|
model = Path(model)
|
|
if "-seg" in model.stem or "segment" in model.parts:
|
|
return "segment"
|
|
elif "-cls" in model.stem or "classify" in model.parts:
|
|
return "classify"
|
|
elif "-pose" in model.stem or "pose" in model.parts:
|
|
return "pose"
|
|
elif "-obb" in model.stem or "obb" in model.parts:
|
|
return "obb"
|
|
elif "detect" in model.parts:
|
|
return "detect"
|
|
|
|
# Unable to determine task from model
|
|
LOGGER.warning(
|
|
"Unable to automatically guess model task, assuming 'task=detect'. "
|
|
"Explicitly define task for your model, i.e. 'task=detect', 'segment', 'classify','pose' or 'obb'."
|
|
)
|
|
return "detect" # assume detect
|