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391 lines
16 KiB
391 lines
16 KiB
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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import torchvision
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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_CONFIG_DICT, DEFAULT_CONFIG_KEYS, LOGGER, colorstr, yaml_load
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from ultralytics.yolo.utils.checks import check_yaml
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from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, initialize_weights, intersect_dicts, make_divisible,
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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 is 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` is a wrapper for `_forward_once` that runs the model on a single scale
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Args:
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x: the input image
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profile: whether to profile the model. Defaults to False
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visualize: if True, will return the intermediate feature maps. Defaults to False
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Returns:
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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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> Forward pass of the network
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Args:
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x: input to the model
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profile: if True, the time taken for each layer will be printed. Defaults to False
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visualize: If True, it will save the feature maps of the model. Defaults to False
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Returns:
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The last layer 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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pass
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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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It takes a model, an input, and a list of times, and it profiles the model on the input, appending
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the time to the list
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Args:
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m: the model
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x: the input image
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dt: list of time taken for each layer
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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.copy() 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.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} {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):
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"""
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> It takes a model and fuses the Conv2d() and BatchNorm2d() layers into a single layer
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Returns:
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The model is being returned.
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"""
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LOGGER.info('Fusing layers... ')
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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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self.info()
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return self
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def info(self, verbose=False, imgsz=640):
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"""
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Prints model information
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Args:
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verbose: if True, prints out the model information. Defaults to False
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imgsz: 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, 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: 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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# YOLOv5 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)) # 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:
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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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# YOLOv5 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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class ClassificationModel(BaseModel):
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# YOLOv5 classification model
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def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml, model, number of classes, cutoff index
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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)
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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):
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# TODO: Create a YOLOv5 classification model from a *.yaml file
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self.model = None
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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 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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from ultralytics.yolo.utils.downloads import attempt_download
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model = Ensemble()
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for w in weights if isinstance(weights, list) else [weights]:
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ckpt = torch.load(attempt_download(w), map_location='cpu') # load
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args = {**DEFAULT_CONFIG_DICT, **ckpt['train_args']} # combine model and default args, preferring model args
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ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model
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# Model compatibility updates
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ckpt.args = {k: v for k, v in args.items() if k in DEFAULT_CONFIG_KEYS} # attach args to model
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ckpt.pt_path = weights # attach *.pt file path to model
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if not hasattr(ckpt, 'stride'):
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ckpt.stride = torch.tensor([32.])
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# Append
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model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode
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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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# Return model
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if len(model) == 1:
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return model[-1]
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# Return ensemble
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print(f'Ensemble created with {weights}\n')
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for k in 'names', 'nc', 'yaml':
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setattr(model, k, getattr(model[0], k))
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model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride
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assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
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return model
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def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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# Parse a YOLOv5 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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no = nc + 4 # number of outputs = classes + box
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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 = eval(m) if isinstance(m, str) else m # eval strings
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for j, a in enumerate(args):
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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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n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
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if m in {
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Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus, BottleneckCSP,
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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 != no: # if not output
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c2 = make_divisible(c2 * gw, 8)
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args = [c1, c2, *args[1:]]
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if m in {BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x}:
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args.insert(2, n) # number of repeats
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n = 1
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elif m is nn.BatchNorm2d:
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args = [ch[f]]
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elif m is Concat:
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c2 = sum(ch[x] for x in f)
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# TODO: channel, gw, gd
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elif m in {Detect, Segment}:
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args.append([ch[x] for x in f])
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if m is Segment:
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args[2] = make_divisible(args[2] * gw, 8)
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else:
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c2 = ch[f]
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m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
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t = str(m)[8:-2].replace('__main__.', '') # module type
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m.np = sum(x.numel() for x in m_.parameters()) # number params
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m_.i, m_.f, m_.type = i, f, t # attach index, 'from' index, type
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if verbose:
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LOGGER.info(f'{i:>3}{str(f):>20}{n_:>3}{m.np:10.0f} {t:<45}{str(args):<30}') # print
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save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
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layers.append(m_)
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if i == 0:
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ch = []
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ch.append(c2)
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return nn.Sequential(*layers), sorted(save)
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def get_model(model='s.pt', pretrained=True):
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# Load a YOLO model locally, from torchvision, or from Ultralytics assets
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if model.endswith(".pt"):
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model = model.split(".")[0]
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if Path(f"{model}.pt").is_file(): # local file
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return attempt_load_weights(f"{model}.pt", device='cpu')
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elif model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0
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return torchvision.models.__dict__[model](weights='IMAGENET1K_V1' if pretrained else None)
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else: # Ultralytics assets
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return attempt_load_weights(f"{model}.pt", device='cpu')
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