1440 lines
59 KiB
Python
1440 lines
59 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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import contextlib
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import pickle
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import types
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from copy import deepcopy
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from pathlib import Path
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import torch
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import torch.nn as nn
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from ultralytics.nn.modules import (
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AIFI,
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C1,
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C2,
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C3,
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C3TR,
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ELAN1,
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OBB,
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PSA,
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SPP,
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SPPELAN,
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SPPF,
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AConv,
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ADown,
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Bottleneck,
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BottleneckCSP,
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C2f,
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C2fAttn,
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C2fCIB,
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C3Ghost,
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C3x,
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CBFuse,
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CBLinear,
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Classify,
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Concat,
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Conv,
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Conv2,
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ConvTranspose,
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Detect,
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DWConv,
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DWConvTranspose2d,
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Focus,
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GhostBottleneck,
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GhostConv,
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HGBlock,
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HGStem,
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ImagePoolingAttn,
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Pose,
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RepC3,
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RepConv,
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RepNCSPELAN4,
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RepVGGDW,
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ResNetLayer,
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RTDETRDecoder,
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MTDETRDecoder,
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SCDown,
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Segment,
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WorldDetect,
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v10Detect,
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)
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from ultralytics.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, emojis, yaml_load
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from ultralytics.utils.checks import check_requirements, check_suffix, check_yaml
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from ultralytics.utils.loss import (
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E2EDetectLoss,
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v8ClassificationLoss,
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v8DetectionLoss,
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v8OBBLoss,
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v8PoseLoss,
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v8SegmentationLoss,
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)
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from ultralytics.utils.ops import make_divisible
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from ultralytics.utils.plotting import feature_visualization
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from ultralytics.utils.torch_utils import (
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fuse_conv_and_bn,
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fuse_deconv_and_bn,
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initialize_weights,
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intersect_dicts,
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model_info,
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scale_img,
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time_sync,
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)
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from ultralytics.nn.modules.swin_transformer import STB
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import torch.nn.utils.parametrize as parametrize
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try:
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import thop
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except ImportError:
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thop = None
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class BaseModel(nn.Module):
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"""The BaseModel class serves as a base class for all the models in the Ultralytics YOLO family."""
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def forward(self, x, epoch=None, *args, **kwargs):
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"""
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Perform forward pass of the model for either training or inference.
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If x is a dict, calculates and returns the loss for training. Otherwise, returns predictions for inference.
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Args:
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x (torch.Tensor | dict): Input tensor for inference, or dict with image tensor and labels for training.
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*args (Any): Variable length argument list.
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**kwargs (Any): Arbitrary keyword arguments.
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Returns:
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(torch.Tensor): Loss if x is a dict (training), or network predictions (inference).
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"""
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if isinstance(x, dict): # for cases of training and validating while training.
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return self.loss(x, epoch=epoch, *args, **kwargs)
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return self.predict(x, *args, **kwargs)
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def predict(self, x, profile=False, visualize=False, augment=False, embed=None):
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"""
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Perform a forward pass through the network.
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Args:
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x (torch.Tensor): The input tensor to the model.
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profile (bool): Print the computation time of each layer if True, defaults to False.
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visualize (bool): Save the feature maps of the model if True, defaults to False.
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augment (bool): Augment image during prediction, defaults to False.
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embed (list, optional): A list of feature vectors/embeddings to return.
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Returns:
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(torch.Tensor): The last output of the model.
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"""
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if augment:
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return self._predict_augment(x)
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return self._predict_once(x, profile, visualize, embed)
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def _predict_once(self, x, profile=False, visualize=False, embed=None):
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"""
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Perform a forward pass through the network.
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Args:
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x (torch.Tensor): The input tensor to the model.
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profile (bool): Print the computation time of each layer if True, defaults to False.
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visualize (bool): Save the feature maps of the model if True, defaults to False.
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embed (list, optional): A list of feature vectors/embeddings to return.
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Returns:
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(torch.Tensor): The last output of the model.
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"""
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y, dt, embeddings = [], [], [] # outputs
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for m in self.model:
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if m.f != -1: # if not from previous layer
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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
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if profile:
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self._profile_one_layer(m, x, dt)
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x = m(x) # run
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y.append(x if m.i in self.save else None) # save output
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if visualize:
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feature_visualization(x, m.type, m.i, save_dir=visualize)
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if embed and m.i in embed:
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embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
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if m.i == max(embed):
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return torch.unbind(torch.cat(embeddings, 1), dim=0)
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return x
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def _predict_augment(self, x):
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"""Perform augmentations on input image x and return augmented inference."""
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LOGGER.warning(
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f"WARNING ⚠️ {self.__class__.__name__} does not support 'augment=True' prediction. "
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f"Reverting to single-scale prediction."
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)
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return self._predict_once(x)
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def _profile_one_layer(self, m, x, dt):
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"""
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Profile the computation time and FLOPs of a single layer of the model on a given input. Appends the results to
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the provided list.
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Args:
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m (nn.Module): The layer to be profiled.
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x (torch.Tensor): The input data to the layer.
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dt (list): A list to store the computation time of the layer.
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Returns:
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None
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"""
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c = m == self.model[-1] and isinstance(x, list) # is final layer list, copy input as inplace fix
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flops = thop.profile(m, inputs=[x.copy() if c else x], verbose=False)[0] / 1e9 * 2 if thop else 0 # GFLOPs
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t = time_sync()
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for _ in range(10):
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m(x.copy() if c else x)
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dt.append((time_sync() - t) * 100)
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if m == self.model[0]:
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LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module")
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LOGGER.info(f"{dt[-1]:10.2f} {flops:10.2f} {m.np:10.0f} {m.type}")
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if c:
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LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
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def fuse(self, verbose=True):
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"""
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Fuse the `Conv2d()` and `BatchNorm2d()` layers of the model into a single layer, in order to improve the
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computation efficiency.
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Returns:
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(nn.Module): The fused model is returned.
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"""
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if not self.is_fused():
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for m in self.model.modules():
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if isinstance(m, (Conv, Conv2, DWConv)) and hasattr(m, "bn"):
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if isinstance(m, Conv2):
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m.fuse_convs()
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m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
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delattr(m, "bn") # remove batchnorm
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m.forward = m.forward_fuse # update forward
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if isinstance(m, ConvTranspose) and hasattr(m, "bn"):
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m.conv_transpose = fuse_deconv_and_bn(m.conv_transpose, m.bn)
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delattr(m, "bn") # remove batchnorm
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m.forward = m.forward_fuse # update forward
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if isinstance(m, RepConv):
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m.fuse_convs()
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m.forward = m.forward_fuse # update forward
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if isinstance(m, RepVGGDW):
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m.fuse()
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m.forward = m.forward_fuse
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self.info(verbose=verbose)
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return self
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def is_fused(self, thresh=10):
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"""
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Check if the model has less than a certain threshold of BatchNorm layers.
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Args:
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thresh (int, optional): The threshold number of BatchNorm layers. Default is 10.
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Returns:
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(bool): True if the number of BatchNorm layers in the model is less than the threshold, False otherwise.
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"""
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bn = tuple(v for k, v in nn.__dict__.items() if "Norm" in k) # normalization layers, i.e. BatchNorm2d()
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return sum(isinstance(v, bn) for v in self.modules()) < thresh # True if < 'thresh' BatchNorm layers in model
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def info(self, detailed=False, verbose=True, imgsz=640):
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"""
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Prints model information.
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Args:
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detailed (bool): if True, prints out detailed information about the model. Defaults to False
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verbose (bool): if True, prints out the model information. Defaults to False
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imgsz (int): the size of the image that the model will be trained on. Defaults to 640
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"""
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return model_info(self, detailed=detailed, verbose=verbose, imgsz=imgsz)
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def _apply(self, fn):
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"""
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Applies a function to all the tensors in the model that are not parameters or registered buffers.
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Args:
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fn (function): the function to apply to the model
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Returns:
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(BaseModel): An updated BaseModel object.
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"""
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self = super()._apply(fn)
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m = self.model[-1] # Detect()
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if isinstance(m, Detect): # includes all Detect subclasses like Segment, Pose, OBB, WorldDetect
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m.stride = fn(m.stride)
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m.anchors = fn(m.anchors)
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m.strides = fn(m.strides)
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return self
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def load(self, weights, verbose=True):
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"""
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Load the weights into the model.
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Args:
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weights (dict | torch.nn.Module): The pre-trained weights to be loaded.
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verbose (bool, optional): Whether to log the transfer progress. Defaults to True.
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"""
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model = weights["model"] if isinstance(weights, dict) else weights # torchvision models are not dicts
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csd = model.float().state_dict() # checkpoint state_dict as FP32
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csd = intersect_dicts(csd, self.state_dict()) # intersect
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self.load_state_dict(csd, strict=False) # load
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if verbose:
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LOGGER.info(f"Transferred {len(csd)}/{len(self.model.state_dict())} items from pretrained weights")
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def loss(self, batch, preds=None):
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"""
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Compute loss.
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Args:
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batch (dict): Batch to compute loss on
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preds (torch.Tensor | List[torch.Tensor]): Predictions.
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"""
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if getattr(self, "criterion", None) is None:
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self.criterion = self.init_criterion()
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preds = self.forward(batch["img"]) if preds is None else preds
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return self.criterion(preds, batch)
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def init_criterion(self):
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"""Initialize the loss criterion for the BaseModel."""
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raise NotImplementedError("compute_loss() needs to be implemented by task heads")
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class DetectionModel(BaseModel):
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"""YOLOv8 detection model."""
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def __init__(self, cfg="yolov8n.yaml", ch=3, nc=None, verbose=True): # model, input channels, number of classes
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"""Initialize the YOLOv8 detection model with the given config and parameters."""
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super().__init__()
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self.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg) # cfg dict
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if self.yaml["backbone"][0][2] == "Silence":
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LOGGER.warning(
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"WARNING ⚠️ YOLOv9 `Silence` module is deprecated in favor of nn.Identity. "
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"Please delete local *.pt file and re-download the latest model checkpoint."
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)
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self.yaml["backbone"][0][2] = "nn.Identity"
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# Define model
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ch = self.yaml["ch"] = self.yaml.get("ch", ch) # input channels
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if nc and nc != self.yaml["nc"]:
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LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
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self.yaml["nc"] = nc # override YAML value
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self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose) # model, savelist
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self.names = {i: f"{i}" for i in range(self.yaml["nc"])} # default names dict
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self.inplace = self.yaml.get("inplace", True)
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self.end2end = getattr(self.model[-1], "end2end", False)
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# Build strides
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m = self.model[-1] # Detect()
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if isinstance(m, Detect): # includes all Detect subclasses like Segment, Pose, OBB, WorldDetect
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s = 256 # 2x min stride
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m.inplace = self.inplace
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def _forward(x):
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"""Performs a forward pass through the model, handling different Detect subclass types accordingly."""
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if self.end2end:
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return self.forward(x)["one2many"]
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return self.forward(x)[0] if isinstance(m, (Segment, Pose, OBB)) else self.forward(x)
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m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))]) # forward
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self.stride = m.stride
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m.bias_init() # only run once
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else:
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self.stride = torch.Tensor([32]) # default stride for i.e. RTDETR
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# Init weights, biases
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initialize_weights(self)
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if verbose:
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self.info()
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LOGGER.info("")
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def _predict_augment(self, x):
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"""Perform augmentations on input image x and return augmented inference and train outputs."""
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if getattr(self, "end2end", False) or self.__class__.__name__ != "DetectionModel":
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LOGGER.warning("WARNING ⚠️ Model does not support 'augment=True', reverting to single-scale prediction.")
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return self._predict_once(x)
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img_size = x.shape[-2:] # height, width
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s = [1, 0.83, 0.67] # scales
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f = [None, 3, None] # flips (2-ud, 3-lr)
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y = [] # outputs
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for si, fi in zip(s, f):
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xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
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yi = super().predict(xi)[0] # forward
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yi = self._descale_pred(yi, fi, si, img_size)
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y.append(yi)
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y = self._clip_augmented(y) # clip augmented tails
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return torch.cat(y, -1), None # augmented inference, train
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@staticmethod
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def _descale_pred(p, flips, scale, img_size, dim=1):
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"""De-scale predictions following augmented inference (inverse operation)."""
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p[:, :4] /= scale # de-scale
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x, y, wh, cls = p.split((1, 1, 2, p.shape[dim] - 4), dim)
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if flips == 2:
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y = img_size[0] - y # de-flip ud
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elif flips == 3:
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x = img_size[1] - x # de-flip lr
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return torch.cat((x, y, wh, cls), dim)
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def _clip_augmented(self, y):
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"""Clip YOLO augmented inference tails."""
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nl = self.model[-1].nl # number of detection layers (P3-P5)
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g = sum(4**x for x in range(nl)) # grid points
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e = 1 # exclude layer count
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i = (y[0].shape[-1] // g) * sum(4**x for x in range(e)) # indices
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y[0] = y[0][..., :-i] # large
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i = (y[-1].shape[-1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices
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y[-1] = y[-1][..., i:] # small
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return y
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def init_criterion(self):
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"""Initialize the loss criterion for the DetectionModel."""
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return E2EDetectLoss(self) if getattr(self, "end2end", False) else v8DetectionLoss(self)
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class OBBModel(DetectionModel):
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"""YOLOv8 Oriented Bounding Box (OBB) model."""
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def __init__(self, cfg="yolov8n-obb.yaml", ch=3, nc=None, verbose=True):
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"""Initialize YOLOv8 OBB model with given config and parameters."""
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super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
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def init_criterion(self):
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"""Initialize the loss criterion for the model."""
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return v8OBBLoss(self)
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class SegmentationModel(DetectionModel):
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"""YOLOv8 segmentation model."""
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def __init__(self, cfg="yolov8n-seg.yaml", ch=3, nc=None, verbose=True):
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"""Initialize YOLOv8 segmentation model with given config and parameters."""
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super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
|
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def init_criterion(self):
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"""Initialize the loss criterion for the SegmentationModel."""
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return v8SegmentationLoss(self)
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class PoseModel(DetectionModel):
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"""YOLOv8 pose model."""
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def __init__(self, cfg="yolov8n-pose.yaml", ch=3, nc=None, data_kpt_shape=(None, None), verbose=True):
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"""Initialize YOLOv8 Pose model."""
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if not isinstance(cfg, dict):
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cfg = yaml_model_load(cfg) # load model YAML
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if any(data_kpt_shape) and list(data_kpt_shape) != list(cfg["kpt_shape"]):
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LOGGER.info(f"Overriding model.yaml kpt_shape={cfg['kpt_shape']} with kpt_shape={data_kpt_shape}")
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cfg["kpt_shape"] = data_kpt_shape
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super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
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def init_criterion(self):
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"""Initialize the loss criterion for the PoseModel."""
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return v8PoseLoss(self)
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class ClassificationModel(BaseModel):
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"""YOLOv8 classification model."""
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def __init__(self, cfg="yolov8n-cls.yaml", ch=3, nc=None, verbose=True):
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"""Init ClassificationModel with YAML, channels, number of classes, verbose flag."""
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super().__init__()
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self._from_yaml(cfg, ch, nc, verbose)
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def _from_yaml(self, cfg, ch, nc, verbose):
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"""Set YOLOv8 model configurations and define the model architecture."""
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self.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg) # cfg dict
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# Define model
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||
ch = self.yaml["ch"] = self.yaml.get("ch", ch) # input channels
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||
if nc and nc != self.yaml["nc"]:
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LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
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self.yaml["nc"] = nc # override YAML value
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elif not nc and not self.yaml.get("nc", None):
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raise ValueError("nc not specified. Must specify nc in model.yaml or function arguments.")
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||
self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose) # model, savelist
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self.stride = torch.Tensor([1]) # no stride constraints
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self.names = {i: f"{i}" for i in range(self.yaml["nc"])} # default names dict
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self.info()
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@staticmethod
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||
def reshape_outputs(model, nc):
|
||
"""Update a TorchVision classification model to class count 'n' if required."""
|
||
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 = nn.Linear(m.linear.in_features, nc)
|
||
elif isinstance(m, nn.Linear): # ResNet, EfficientNet
|
||
if m.out_features != nc:
|
||
setattr(model, name, nn.Linear(m.in_features, nc))
|
||
elif isinstance(m, nn.Sequential):
|
||
types = [type(x) for x in m]
|
||
if nn.Linear in types:
|
||
i = len(types) - 1 - types[::-1].index(nn.Linear) # last nn.Linear index
|
||
if m[i].out_features != nc:
|
||
m[i] = nn.Linear(m[i].in_features, nc)
|
||
elif nn.Conv2d in types:
|
||
i = len(types) - 1 - types[::-1].index(nn.Conv2d) # last nn.Conv2d index
|
||
if m[i].out_channels != nc:
|
||
m[i] = 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:
|
||
cfg (str): The configuration file path or preset string. Default is 'rtdetr-l.yaml'.
|
||
ch (int): Number of input channels. Default is 3 (RGB).
|
||
nc (int, optional): Number of classes for object detection. Default is None.
|
||
verbose (bool): Specifies if summary statistics are shown during initialization. Default is True.
|
||
|
||
Methods:
|
||
init_criterion: Initializes the criterion used for loss calculation.
|
||
loss: Computes and returns the loss during training.
|
||
predict: Performs a forward pass through the network and returns the output.
|
||
"""
|
||
|
||
def __init__(self, cfg="rtdetr-l.yaml", ch=3, nc=None, verbose=True):
|
||
"""
|
||
Initialize the RTDETRDetectionModel.
|
||
|
||
Args:
|
||
cfg (str): Configuration file name or path.
|
||
ch (int): Number of input channels.
|
||
nc (int, optional): Number of classes. Defaults to None.
|
||
verbose (bool, optional): Print additional information during initialization. Defaults to True.
|
||
"""
|
||
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. Defaults to None.
|
||
|
||
Returns:
|
||
(tuple): A tuple containing the total loss and 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, optional): If True, profile the computation time for each layer. Defaults to False.
|
||
visualize (bool, optional): If True, save feature maps for visualization. Defaults to False.
|
||
batch (dict, optional): Ground truth data for evaluation. Defaults to None.
|
||
augment (bool, optional): If True, perform data augmentation during inference. Defaults to False.
|
||
embed (list, optional): A list of feature vectors/embeddings to return.
|
||
|
||
Returns:
|
||
(torch.Tensor): Model's output tensor.
|
||
"""
|
||
y, dt, embeddings = [], [], [] # outputs
|
||
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 embed and m.i in embed:
|
||
embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
|
||
if m.i == max(embed):
|
||
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
|
||
|
||
from collections import deque
|
||
import numpy as np
|
||
class ConvergenceAwareWeightScheduler:
|
||
def __init__(self, num_tasks, k=10, epsilon=0.1):
|
||
self.num_tasks = num_tasks
|
||
self.k = k # 滑动窗口长度
|
||
self.epsilon = epsilon # 基础权重
|
||
self.loss_history = [deque(maxlen=k) for _ in range(num_tasks)] # 各任务损失历史记录
|
||
self.consecutive_neg_slope = [0] * num_tasks # 连续负斜率计数器
|
||
self.max_penalty = 2.0
|
||
|
||
def compute_slope(self, loss_values):
|
||
if len(loss_values) < 2:
|
||
return 0.0
|
||
|
||
n = len(loss_values)
|
||
x = np.arange(n, dtype=np.float32)
|
||
y = np.array(loss_values, dtype=np.float32)
|
||
|
||
# 线性递增权重：越新的数据权重越高 (从0.1到1.0线性增长)
|
||
weights = np.linspace(0.1, 1.0, n, dtype=np.float32)
|
||
|
||
# 加权均值计算
|
||
weighted_sum = np.sum(weights)
|
||
x_weighted_mean = np.sum(weights * x) / weighted_sum
|
||
y_weighted_mean = np.sum(weights * y) / weighted_sum
|
||
|
||
# 加权协方差计算
|
||
numerator = np.sum(weights * (x - x_weighted_mean) * (y - y_weighted_mean))
|
||
denominator = np.sum(weights * (x - x_weighted_mean) ** 2) + 1e-8 # 避免除零
|
||
|
||
a = numerator / denominator
|
||
return float(a)
|
||
|
||
def update_weights(self, losses):
|
||
# 更新EMA损失并记录历史
|
||
|
||
# 记录EMA损失历史
|
||
for i in range(self.num_tasks):
|
||
self.loss_history[i].append(losses[i])
|
||
|
||
# 计算损失下降量Delta L
|
||
delta_L = []
|
||
if len(self.loss_history[0]) >= self.k:
|
||
for i in range(self.num_tasks):
|
||
slope = self.compute_slope(self.loss_history[i])
|
||
delta_L_i = -slope # 负斜率代表下降，值越大说明下降越快
|
||
delta_L.append(delta_L_i)
|
||
else:
|
||
delta_L = [1.0] * self.num_tasks # 初始阶段均等权重
|
||
|
||
for i in range(self.num_tasks):
|
||
if delta_L[i] < 0: # 负斜率代表损失上升
|
||
self.consecutive_neg_slope[i] += 1
|
||
else:
|
||
self.consecutive_neg_slope[i] = 0
|
||
|
||
min_value = min(delta_L)
|
||
max_value = max(delta_L)
|
||
normal = max_value - min_value + 1e-8
|
||
|
||
# 分配基础权重
|
||
weights = [max(1 - (d - min_value) / normal, self.epsilon) for d in delta_L]
|
||
|
||
# 对连续发散任务施加惩罚
|
||
high_neg_indices = [i for i, cnt in enumerate(self.consecutive_neg_slope) if cnt >= 3]
|
||
for i in high_neg_indices:
|
||
penalty = min(1 + self.consecutive_neg_slope[i]/10, self.max_penalty)
|
||
weights[i] *= penalty
|
||
|
||
# 权重归一化
|
||
total_weight = sum(weights)
|
||
normalized_weights = [w * self.num_tasks / total_weight for w in weights]
|
||
|
||
return normalized_weights
|
||
|
||
|
||
class MTDETRModel(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:
|
||
cfg (str): The configuration file path or preset string. Default is 'rtdetr-l.yaml'.
|
||
ch (int): Number of input channels. Default is 3 (RGB).
|
||
nc (int, optional): Number of classes for object detection. Default is None.
|
||
verbose (bool): Specifies if summary statistics are shown during initialization. Default is True.
|
||
|
||
Methods:
|
||
init_criterion: Initializes the criterion used for loss calculation.
|
||
loss: Computes and returns the loss during training.
|
||
predict: Performs a forward pass through the network and returns the output.
|
||
"""
|
||
|
||
def __init__(self, cfg="rtdetr-l.yaml", ch=3, nc=None, verbose=True):
|
||
"""
|
||
Initialize the RTDETRDetectionModel.
|
||
|
||
Args:
|
||
cfg (str): Configuration file name or path.
|
||
ch (int): Number of input channels.
|
||
nc (int, optional): Number of classes. Defaults to None.
|
||
verbose (bool, optional): Print additional information during initialization. Defaults to True.
|
||
"""
|
||
super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
|
||
self.criterion = self.init_criterion()
|
||
|
||
# ###### JW init the learnable parameters for dynamic weight.
|
||
# self.log_var_det = nn.Parameter(torch.zeros(1))
|
||
# self.log_var_da = nn.Parameter(torch.zeros(1))
|
||
# self.log_var_ll = nn.Parameter(torch.zeros(1))
|
||
# if nc is None:
|
||
# self.scheduler = ConvergenceAwareWeightScheduler(num_tasks=cfg['nc'], k=10, epsilon=0.1)
|
||
# else:
|
||
# self.scheduler = ConvergenceAwareWeightScheduler(num_tasks=nc, k=10, epsilon=0.1)
|
||
# self.alpha = 0.7 # EMA平滑系数，可视需要修改
|
||
# self.mu_det = None
|
||
# self.mu_da = None
|
||
# self.mu_ll = None
|
||
|
||
def init_criterion(self):
|
||
"""Initialize the loss criterion for the RTDETRDetectionModel."""
|
||
from ultralytics.models.utils.loss import MTDETRDLoss
|
||
### JW transfer the nc_list to loss
|
||
return MTDETRDLoss(nc=self.yaml['nc_list'], use_vfl=True)
|
||
|
||
def loss(self, batch, preds=None, epoch=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. Defaults to None.
|
||
|
||
Returns:
|
||
(tuple): A tuple containing the total loss and main three losses in a tensor.
|
||
"""
|
||
|
||
img = batch["img"]
|
||
# NOTE: preprocess gt_bbox and gt_labels to list.
|
||
bs = len(img)
|
||
|
||
### JW split detection and segmentation label.
|
||
type_task = batch['type_task'][0]
|
||
segmentation_classes = torch.tensor(type_task['segmentation'])
|
||
detection_classes = torch.tensor(type_task['detection'])
|
||
# segmentation_indices = torch.where(torch.isin(batch["cls"], segmentation_classes.to(device=batch["cls"].device)))[0]
|
||
detection_indices = torch.where(torch.isin(batch["cls"], detection_classes.to(device=batch["cls"].device)))[0]
|
||
# if segmentation_indices.numel() != 0:
|
||
# gt_groups_segmentation = [(batch["batch_idx"][segmentation_indices] == i).sum().item() for i in range(bs)]
|
||
# targets_segmentation = {
|
||
# "cls": batch["cls"][segmentation_indices].to(img.device, dtype=torch.long).view(-1),
|
||
# "batch_idx": batch["batch_idx"][segmentation_indices].to(img.device, dtype=torch.long).view(-1),
|
||
# "merge_mask": batch["merge_mask"].to(img.device),
|
||
# "gt_groups": gt_groups_segmentation,
|
||
# }
|
||
gt_groups_detection = [(batch["batch_idx"][detection_indices] == i).sum().item() for i in range(bs)]
|
||
targets_detection = {
|
||
"cls": batch["cls"][detection_indices].to(img.device, dtype=torch.long).view(-1),
|
||
"bboxes": batch["bboxes"][detection_indices].to(device=img.device),
|
||
"batch_idx": batch["batch_idx"][detection_indices].to(img.device, dtype=torch.long).view(-1),
|
||
"gt_groups": gt_groups_detection,
|
||
}
|
||
|
||
targets_segmentation = {
|
||
"segmentation_classes": segmentation_classes.to(img.device),
|
||
"merge_mask": batch["merge_mask"].to(img.device),
|
||
}
|
||
|
||
# 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_detection) if preds is None else preds
|
||
dec_bboxes, dec_scores, enc_bboxes, enc_scores, dn_meta, seg_masks, aux_list = 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_detection, dn_bboxes=dn_bboxes, dn_scores=dn_scores, dn_meta=dn_meta, seg_mask=[seg_masks, aux_list], seg_batch=targets_segmentation
|
||
)
|
||
|
||
# Detection
|
||
det_losses = [
|
||
'loss_class', 'loss_bbox', 'loss_giou',
|
||
'loss_class_aux', 'loss_bbox_aux', 'loss_giou_aux',
|
||
'loss_class_dn', 'loss_bbox_dn', 'loss_giou_dn',
|
||
'loss_class_aux_dn', 'loss_bbox_aux_dn', 'loss_giou_aux_dn'
|
||
]
|
||
L_det = sum([loss[key] for key in det_losses])
|
||
|
||
# seg
|
||
da_losses = ['da_fl_loss', 'da_tversky_loss']
|
||
da_seg = sum([loss[key] for key in da_losses])
|
||
|
||
ll_losses = ['ll_fl_loss', 'll_tversky_loss']
|
||
ll_seg = sum([loss[key] for key in ll_losses])
|
||
|
||
# total_loss = L_det + da_seg + ll_seg
|
||
return [L_det, da_seg, ll_seg], torch.as_tensor(
|
||
[L_det.detach(), da_seg.detach(), ll_seg.detach()],
|
||
device=img.device
|
||
), preds[-1]
|
||
|
||
|
||
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, optional): If True, profile the computation time for each layer. Defaults to False.
|
||
visualize (bool, optional): If True, save feature maps for visualization. Defaults to False.
|
||
batch (dict, optional): Ground truth data for evaluation. Defaults to None.
|
||
augment (bool, optional): If True, perform data augmentation during inference. Defaults to False.
|
||
embed (list, optional): A list of feature vectors/embeddings to return.
|
||
|
||
Returns:
|
||
(torch.Tensor): Model's output tensor.
|
||
"""
|
||
y, dt, embeddings = [], [], [] # outputs
|
||
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 embed and m.i in embed:
|
||
embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
|
||
if m.i == max(embed):
|
||
return torch.unbind(torch.cat(embeddings, 1), dim=0)
|
||
head = self.model[-1]
|
||
x = head([y[j] for j in head.f], batch)
|
||
return x
|
||
|
||
|
||
class WorldModel(DetectionModel):
|
||
"""YOLOv8 World Model."""
|
||
|
||
def __init__(self, cfg="yolov8s-world.yaml", ch=3, nc=None, verbose=True):
|
||
"""Initialize YOLOv8 world model with given config and parameters."""
|
||
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."""
|
||
try:
|
||
import clip
|
||
except ImportError:
|
||
check_requirements("git+https://github.com/ultralytics/CLIP.git")
|
||
import clip
|
||
|
||
if (
|
||
not getattr(self, "clip_model", None) and cache_clip_model
|
||
): # for backwards compatibility of models lacking clip_model attribute
|
||
self.clip_model = clip.load("ViT-B/32")[0]
|
||
model = self.clip_model if cache_clip_model else clip.load("ViT-B/32")[0]
|
||
device = next(model.parameters()).device
|
||
text_token = clip.tokenize(text).to(device)
|
||
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 / txt_feats.norm(p=2, dim=-1, keepdim=True)
|
||
self.txt_feats = txt_feats.reshape(-1, len(text), txt_feats.shape[-1])
|
||
self.model[-1].nc = len(text)
|
||
|
||
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, optional): If True, profile the computation time for each layer. Defaults to False.
|
||
visualize (bool, optional): If True, save feature maps for visualization. Defaults to False.
|
||
txt_feats (torch.Tensor): The text features, use it if it's given. Defaults to None.
|
||
augment (bool, optional): If True, perform data augmentation during inference. Defaults to False.
|
||
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):
|
||
txt_feats = txt_feats.repeat(len(x), 1, 1)
|
||
ori_txt_feats = txt_feats.clone()
|
||
y, dt, embeddings = [], [], [] # outputs
|
||
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 embed and m.i in embed:
|
||
embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
|
||
if m.i == max(embed):
|
||
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]): 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 Ensemble(nn.ModuleList):
|
||
"""Ensemble of models."""
|
||
|
||
def __init__(self):
|
||
"""Initialize an ensemble of models."""
|
||
super().__init__()
|
||
|
||
def forward(self, x, augment=False, profile=False, visualize=False):
|
||
"""Function generates the YOLO network's final layer."""
|
||
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.
|
||
|
||
Example:
|
||
```python
|
||
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."""
|
||
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):
|
||
"""
|
||
Attempts 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.
|
||
|
||
Example:
|
||
```python
|
||
from ultralytics.nn.tasks import torch_safe_load
|
||
|
||
ckpt, file = torch_safe_load("path/to/best.pt", safe_only=True)
|
||
```
|
||
|
||
Returns:
|
||
ckpt (dict): The loaded model checkpoint.
|
||
file (str): The loaded filename
|
||
"""
|
||
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=yolov8n.pt'"
|
||
)
|
||
) from e
|
||
LOGGER.warning(
|
||
f"WARNING ⚠️ {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=yolov8n.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"WARNING ⚠️ 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):
|
||
"""Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a."""
|
||
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 = 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, 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):
|
||
"""Loads a single model weights."""
|
||
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 = 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, 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): # model_dict, input_channels(3)
|
||
"""Parse a YOLO model.yaml dictionary into a PyTorch model."""
|
||
import ast
|
||
|
||
# Args
|
||
max_channels = float("inf")
|
||
nc, nc_list, act, scales = (d.get(x) for x in ("nc", "nc_list", "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"WARNING ⚠️ 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 = 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
|
||
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 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 {
|
||
Classify,
|
||
Conv,
|
||
ConvTranspose,
|
||
GhostConv,
|
||
Bottleneck,
|
||
GhostBottleneck,
|
||
SPP,
|
||
SPPF,
|
||
DWConv,
|
||
Focus,
|
||
BottleneckCSP,
|
||
C1,
|
||
C2,
|
||
C2f,
|
||
RepNCSPELAN4,
|
||
ELAN1,
|
||
ADown,
|
||
AConv,
|
||
SPPELAN,
|
||
C2fAttn,
|
||
C3,
|
||
C3TR,
|
||
C3Ghost,
|
||
nn.ConvTranspose2d,
|
||
DWConvTranspose2d,
|
||
C3x,
|
||
RepC3,
|
||
PSA,
|
||
SCDown,
|
||
C2fCIB,
|
||
}:
|
||
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:
|
||
args[1] = make_divisible(min(args[1], max_channels // 2) * width, 8) # embed channels
|
||
args[2] = int(
|
||
max(round(min(args[2], max_channels // 2 // 32)) * width, 1) if args[2] > 1 else args[2]
|
||
) # num heads
|
||
|
||
args = [c1, c2, *args[1:]]
|
||
if m in {BottleneckCSP, C1, C2, C2f, C2fAttn, C3, C3TR, C3Ghost, C3x, RepC3, C2fCIB}:
|
||
args.insert(2, n) # number of repeats
|
||
n = 1
|
||
elif m is AIFI:
|
||
args = [ch[f], *args]
|
||
elif m is STB:
|
||
args = [ch[f], *args]
|
||
elif m in {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 nn.BatchNorm2d:
|
||
args = [ch[f]]
|
||
elif m is Concat:
|
||
c2 = sum(ch[x] for x in f)
|
||
elif m in {Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn, v10Detect}:
|
||
args.append([ch[x] for x in f])
|
||
if m is Segment:
|
||
args[2] = make_divisible(min(args[2], max_channels) * width, 8)
|
||
elif m is RTDETRDecoder: # special case, channels arg must be passed in index 1
|
||
args = [args[0]['detection']] ### JW split detection and segmentation nc.
|
||
args.insert(1, [ch[x] for x in f])
|
||
elif m is MTDETRDecoder: # special case, channels arg must be passed in index 1
|
||
args_list = [args[0]['detection']] ### JW split detection and segmentation nc.
|
||
args_list.insert(1, [ch[x] for x in f])
|
||
args_list.append(args[0]['segmentation'])
|
||
args = args_list
|
||
elif m is CBLinear:
|
||
c2 = args[0]
|
||
c1 = ch[f]
|
||
args = [c1, c2, *args[1:]]
|
||
elif m is CBFuse:
|
||
c2 = ch[f[-1]]
|
||
else:
|
||
c2 = ch[f]
|
||
|
||
m_ = 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 nn.Sequential(*layers), sorted(save)
|
||
|
||
|
||
def yaml_model_load(path):
|
||
"""Load a YOLOv8 model from a YAML file."""
|
||
import re
|
||
|
||
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(.+)?$", r"\1\2-p6\3", path.stem)
|
||
LOGGER.warning(f"WARNING ⚠️ Ultralytics YOLO P6 models now use -p6 suffix. Renaming {path.stem} to {new_stem}.")
|
||
path = path.with_name(new_stem + path.suffix)
|
||
|
||
unified_path = re.sub(r"(\d+)([nslmx])(.+)?$", 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):
|
||
"""
|
||
Takes a path to a YOLO model's YAML file as input and extracts the size character of the model's scale. The function
|
||
uses regular expression matching to find the pattern of the model scale in the YAML file name, which is denoted by
|
||
n, s, m, l, or x. The function returns the size character of the model scale as a string.
|
||
|
||
Args:
|
||
model_path (str | Path): The path to the YOLO model's YAML file.
|
||
|
||
Returns:
|
||
(str): The size character of the model's scale, which can be n, s, m, l, or x.
|
||
"""
|
||
with contextlib.suppress(AttributeError):
|
||
import re
|
||
|
||
return re.search(r"yolov\d+([nslmx])", Path(model_path).stem).group(1) # n, s, m, l, or x
|
||
return ""
|
||
|
||
|
||
def guess_model_task(model):
|
||
"""
|
||
Guess the task of a PyTorch model from its architecture or configuration.
|
||
|
||
Args:
|
||
model (nn.Module | dict): PyTorch model or model configuration in YAML format.
|
||
|
||
Returns:
|
||
(str): Task of the model ('detect', 'segment', 'classify', 'pose').
|
||
|
||
Raises:
|
||
SyntaxError: If the task of the model could not be determined.
|
||
"""
|
||
|
||
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 m == "segment":
|
||
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, 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):
|
||
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, 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(
|
||
"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
|
||
|
||
|
||
|
||
|
||
|
||
class ClampParametrization(nn.Module):
|
||
def __init__(self, min_val=-0.5, max_val=1.5):
|
||
super().__init__()
|
||
self.min_val = min_val
|
||
self.max_val = max_val
|
||
|
||
def forward(self, X):
|
||
return torch.clamp(X, min=self.min_val, max=self.max_val)
|
||
|
||
def right_inverse(self, X):
|
||
return torch.clamp(X, min=self.min_val, max=self.max_val) # 初始化时也应用约束
|