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# Ultralytics YOLO 🚀, GPL-3.0 license |
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import contextlib |
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from copy import deepcopy |
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from pathlib import Path |
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import thop |
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import torch |
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import torch.nn as nn |
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from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify, |
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Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus, |
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GhostBottleneck, GhostConv, Segment) |
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from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, colorstr, emojis, yaml_load |
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from ultralytics.yolo.utils.checks import check_requirements, check_yaml |
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from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, fuse_deconv_and_bn, initialize_weights, |
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intersect_dicts, make_divisible, model_info, scale_img, time_sync) |
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class BaseModel(nn.Module): |
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""" |
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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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""" |
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def forward(self, x, profile=False, visualize=False): |
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""" |
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Forward pass of the model on a single scale. |
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Wrapper for `_forward_once` method. |
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Args: |
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x (torch.Tensor): The input image tensor |
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profile (bool): Whether to profile the model, defaults to False |
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visualize (bool): Whether to return the intermediate feature maps, defaults to False |
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Returns: |
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(torch.Tensor): The output of the network. |
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""" |
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return self._forward_once(x, profile, visualize) |
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def _forward_once(self, x, profile=False, visualize=False): |
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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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Returns: |
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(torch.Tensor): The last output of the model. |
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""" |
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y, dt = [], [] # 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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LOGGER.info('visualize feature not yet supported') |
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# TODO: feature_visualization(x, m.type, m.i, save_dir=visualize) |
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return 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. |
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Appends the results to 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] # is final layer, copy input as inplace fix |
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o = thop.profile(m, inputs=[x.clone() if c else x], verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs |
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t = time_sync() |
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for _ in range(10): |
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m(x.clone() 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} {o: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, DWConv)) and hasattr(m, 'bn'): |
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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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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, verbose=True, imgsz=640): |
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""" |
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Prints model information |
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Args: |
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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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model_info(self, verbose=verbose, imgsz=imgsz) |
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def _apply(self, fn): |
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""" |
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`_apply()` is a function that applies a function to all the tensors in the model that are not |
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parameters or registered buffers |
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Args: |
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fn: the function to apply to the model |
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Returns: |
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A model that is a Detect() 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, Segment)): |
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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): |
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""" |
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This function loads the weights of the model from a file |
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Args: |
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weights (str): The weights to load into the model. |
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""" |
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# Force all tasks to implement this function |
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raise NotImplementedError('This function needs to be implemented by derived classes!') |
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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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super().__init__() |
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self.yaml = cfg if isinstance(cfg, dict) else yaml_load(check_yaml(cfg), append_filename=True) # 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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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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# Build strides |
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m = self.model[-1] # Detect() |
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if isinstance(m, (Detect, Segment)): |
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s = 256 # 2x min stride |
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m.inplace = self.inplace |
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forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) 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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# 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 forward(self, x, augment=False, profile=False, visualize=False): |
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if augment: |
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return self._forward_augment(x) # augmented inference, None |
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return self._forward_once(x, profile, visualize) # single-scale inference, train |
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def _forward_augment(self, 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 = self._forward_once(xi)[0] # forward |
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# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save |
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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 YOLOv5 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 load(self, weights, verbose=True): |
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csd = weights.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 and RANK == -1: |
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LOGGER.info(f'Transferred {len(csd)}/{len(self.model.state_dict())} items from pretrained weights') |
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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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super().__init__(cfg, ch, nc, verbose) |
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def _forward_augment(self, x): |
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raise NotImplementedError('WARNING ⚠️ SegmentationModel has not supported augment inference yet!') |
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class ClassificationModel(BaseModel): |
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# YOLOv8 classification model |
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def __init__(self, |
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cfg=None, |
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model=None, |
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ch=3, |
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nc=None, |
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cutoff=10, |
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verbose=True): # yaml, model, channels, number of classes, cutoff index, verbose flag |
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super().__init__() |
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self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg, ch, nc, verbose) |
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def _from_detection_model(self, model, nc=1000, cutoff=10): |
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# Create a YOLOv5 classification model from a YOLOv5 detection model |
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from ultralytics.nn.autobackend import AutoBackend |
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if isinstance(model, AutoBackend): |
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model = model.model # unwrap DetectMultiBackend |
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model.model = model.model[:cutoff] # backbone |
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m = model.model[-1] # last layer |
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ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels # ch into module |
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c = Classify(ch, nc) # Classify() |
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c.i, c.f, c.type = m.i, m.f, 'models.common.Classify' # index, from, type |
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model.model[-1] = c # replace |
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self.model = model.model |
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self.stride = model.stride |
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self.save = [] |
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self.nc = nc |
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def _from_yaml(self, cfg, ch, nc, verbose): |
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self.yaml = cfg if isinstance(cfg, dict) else yaml_load(check_yaml(cfg), append_filename=True) # 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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def load(self, weights): |
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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() |
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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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@staticmethod |
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def reshape_outputs(model, nc): |
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# Update a TorchVision classification model to class count 'n' if required |
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name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1] # last module |
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if isinstance(m, Classify): # YOLO Classify() head |
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if m.linear.out_features != nc: |
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m.linear = nn.Linear(m.linear.in_features, nc) |
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elif isinstance(m, nn.Linear): # ResNet, EfficientNet |
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if m.out_features != nc: |
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setattr(model, name, nn.Linear(m.in_features, nc)) |
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elif isinstance(m, nn.Sequential): |
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types = [type(x) for x in m] |
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if nn.Linear in types: |
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i = types.index(nn.Linear) # nn.Linear index |
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if m[i].out_features != nc: |
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m[i] = nn.Linear(m[i].in_features, nc) |
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elif nn.Conv2d in types: |
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i = types.index(nn.Conv2d) # nn.Conv2d index |
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if m[i].out_channels != nc: |
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m[i] = nn.Conv2d(m[i].in_channels, nc, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None) |
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# Functions ------------------------------------------------------------------------------------------------------------ |
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def torch_safe_load(weight): |
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""" |
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This function attempts to load a PyTorch model with the torch.load() function. If a ModuleNotFoundError is raised, |
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it catches the error, logs a warning message, and attempts to install the missing module via the |
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check_requirements() function. After installation, the function again attempts to load the model using torch.load(). |
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Args: |
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weight (str): The file path of the PyTorch model. |
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Returns: |
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The loaded PyTorch model. |
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""" |
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from ultralytics.yolo.utils.downloads import attempt_download_asset |
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file = attempt_download_asset(weight) # search online if missing locally |
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try: |
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return torch.load(file, map_location='cpu'), file # load |
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except ModuleNotFoundError as e: # e.name is missing module name |
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if e.name == 'models': |
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raise TypeError( |
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emojis(f'ERROR ❌️ {weight} appears to be an Ultralytics YOLOv5 model originally trained ' |
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f'with https://github.com/ultralytics/yolov5.\nThis model is NOT forwards compatible with ' |
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f'YOLOv8 at https://github.com/ultralytics/ultralytics.' |
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f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to " |
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f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'")) from e |
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LOGGER.warning(f"WARNING ⚠️ {weight} appears to require '{e.name}', which is not in ultralytics requirements." |
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f"\nAutoInstall will run now for '{e.name}' but this feature will be removed in the future." |
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f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to " |
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f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'") |
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check_requirements(e.name) # install missing module |
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return torch.load(file, map_location='cpu'), file # load |
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def attempt_load_weights(weights, device=None, inplace=True, fuse=False): |
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# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a |
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ensemble = Ensemble() |
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for w in weights if isinstance(weights, list) else [weights]: |
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ckpt, w = torch_safe_load(w) # load ckpt |
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args = {**DEFAULT_CFG_DICT, **ckpt['train_args']} # combine model and default args, preferring model args |
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model = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model |
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# Model compatibility updates |
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model.args = args # attach args to model |
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model.pt_path = w # attach *.pt file path to model |
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model.task = guess_model_task(model) |
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if not hasattr(model, 'stride'): |
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model.stride = torch.tensor([32.]) |
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# Append |
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ensemble.append(model.fuse().eval() if fuse and hasattr(model, 'fuse') else model.eval()) # model in eval mode |
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# Module compatibility updates |
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for m in ensemble.modules(): |
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t = type(m) |
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if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Segment): |
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m.inplace = inplace # torch 1.7.0 compatibility |
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elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'): |
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m.recompute_scale_factor = None # torch 1.11.0 compatibility |
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# Return model |
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if len(ensemble) == 1: |
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return ensemble[-1] |
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# Return ensemble |
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LOGGER.info(f'Ensemble created with {weights}\n') |
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for k in 'names', 'nc', 'yaml': |
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setattr(ensemble, k, getattr(ensemble[0], k)) |
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ensemble.stride = ensemble[torch.argmax(torch.tensor([m.stride.max() for m in ensemble])).int()].stride |
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assert all(ensemble[0].nc == m.nc for m in ensemble), f'Models differ in class counts: {[m.nc for m in ensemble]}' |
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return ensemble |
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def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False): |
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# Loads a single model weights |
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ckpt, weight = torch_safe_load(weight) # load ckpt |
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args = {**DEFAULT_CFG_DICT, **ckpt['train_args']} # combine model and default args, preferring model args |
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model = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model |
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|
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# Model compatibility updates |
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model.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS} # attach args to model |
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model.pt_path = weight # attach *.pt file path to model |
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model.task = guess_model_task(model) |
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if not hasattr(model, 'stride'): |
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model.stride = torch.tensor([32.]) |
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model = model.fuse().eval() if fuse and hasattr(model, 'fuse') else model.eval() # model in eval mode |
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|
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# Module compatibility updates |
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for m in model.modules(): |
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t = type(m) |
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if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Segment): |
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m.inplace = inplace # torch 1.7.0 compatibility |
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elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'): |
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m.recompute_scale_factor = None # torch 1.11.0 compatibility |
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|
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# Return model and ckpt |
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return model, ckpt |
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def parse_model(d, ch, verbose=True): # model_dict, input_channels(3) |
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# Parse a YOLO model.yaml dictionary |
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if verbose: |
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LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10} {'module':<45}{'arguments':<30}") |
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nc, gd, gw, act = d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation') |
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if act: |
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Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() |
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if verbose: |
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LOGGER.info(f"{colorstr('activation:')} {act}") # print |
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ch = [ch] |
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layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out |
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for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args |
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m = getattr(torch.nn, m[3:]) if 'nn.' in m else globals()[m] # get module |
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for j, a in enumerate(args): |
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# TODO: re-implement with eval() removal if possible |
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# args[j] = (locals()[a] if a in locals() else ast.literal_eval(a)) if isinstance(a, str) else a |
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with contextlib.suppress(NameError): |
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args[j] = eval(a) if isinstance(a, str) else a # eval strings |
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|
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n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain |
|
|
if m in (Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus, |
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|
BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x): |
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|
c1, c2 = ch[f], args[0] |
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if c2 != nc: # if c2 not equal to number of classes (i.e. for Classify() output) |
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|
c2 = make_divisible(c2 * gw, 8) |
|
|
|
|
|
args = [c1, c2, *args[1:]] |
|
|
if m in (BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x): |
|
|
args.insert(2, n) # number of repeats |
|
|
n = 1 |
|
|
elif m is nn.BatchNorm2d: |
|
|
args = [ch[f]] |
|
|
elif m is Concat: |
|
|
c2 = sum(ch[x] for x in f) |
|
|
elif m in (Detect, Segment): |
|
|
args.append([ch[x] for x in f]) |
|
|
if m is Segment: |
|
|
args[2] = make_divisible(args[2] * gw, 8) |
|
|
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 guess_model_task(model): |
|
|
""" |
|
|
Guess the task of a PyTorch model from its architecture or configuration. |
|
|
|
|
|
Args: |
|
|
model (nn.Module) or (dict): PyTorch model or model configuration in YAML format. |
|
|
|
|
|
Returns: |
|
|
str: Task of the model ('detect', 'segment', 'classify'). |
|
|
|
|
|
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 m == 'detect': |
|
|
return 'detect' |
|
|
if m == 'segment': |
|
|
return 'segment' |
|
|
|
|
|
# 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, Detect): |
|
|
return 'detect' |
|
|
elif isinstance(m, Segment): |
|
|
return 'segment' |
|
|
elif isinstance(m, Classify): |
|
|
return 'classify' |
|
|
|
|
|
# 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 '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', 'task=segment' or 'task=classify'.") |
|
|
return 'detect' # assume detect
|
|
|
|
