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217 lines
9.7 KiB
217 lines
9.7 KiB
import time
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from copy import deepcopy
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import thop
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import torch.nn as nn
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from ultralytics.yolo.utils import LOGGER
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from ultralytics.yolo.utils.anchors import check_anchor_order
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from ultralytics.yolo.utils.modeling import parse_model
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from ultralytics.yolo.utils.modeling.modules import *
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from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, initialize_weights, intersect_state_dicts, model_info,
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scale_img, time_sync)
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class BaseModel(nn.Module):
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# YOLOv5 base model
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def forward(self, x, profile=False, visualize=False):
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return self._forward_once(x, profile, visualize) # single-scale inference, train
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def _forward_once(self, x, profile=False, visualize=False):
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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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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): # fuse model Conv2d() + BatchNorm2d() layers
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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, img_size=640): # print model information
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model_info(self, verbose, img_size)
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def _apply(self, fn):
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# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
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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.grid = list(map(fn, m.grid))
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if isinstance(m.anchor_grid, list):
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m.anchor_grid = list(map(fn, m.anchor_grid))
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return self
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def load(self, weights):
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# Force all tasks 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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# YOLO detection model
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def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes
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super().__init__()
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if isinstance(cfg, dict):
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self.yaml = cfg # model dict
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else: # is *.yaml
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import yaml # for torch hub
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self.yaml_file = Path(cfg).name
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with open(cfg, encoding='ascii', errors='ignore') as f:
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self.yaml = yaml.safe_load(f) # model 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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if anchors:
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LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
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self.yaml['anchors'] = round(anchors) # override yaml value
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self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
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self.names = [str(i) for i in range(self.yaml['nc'])] # default names
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self.inplace = self.yaml.get('inplace', True)
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# Build strides, anchors
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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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check_anchor_order(m)
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m.anchors /= m.stride.view(-1, 1, 1)
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self.stride = m.stride
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self._initialize_biases() # only run once
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# Init weights, biases
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initialize_weights(self)
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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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def _descale_pred(self, p, flips, scale, img_size):
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# de-scale predictions following augmented inference (inverse operation)
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if self.inplace:
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p[..., :4] /= scale # de-scale
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if flips == 2:
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p[..., 1] = img_size[0] - p[..., 1] # de-flip ud
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elif flips == 3:
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p[..., 0] = img_size[1] - p[..., 0] # de-flip lr
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else:
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x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale
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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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p = torch.cat((x, y, wh, p[..., 4:]), -1)
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return p
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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 _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
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# https://arxiv.org/abs/1708.02002 section 3.3
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# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
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m = self.model[-1] # Detect() module
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for mi, s in zip(m.m, m.stride): # from
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b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
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b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
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b.data[:, 5:5 + m.nc] += math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum()) # cls
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mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
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def load(self, weights):
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ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak
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csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
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csd = intersect_state_dicts(csd, self.state_dict()) # intersect
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self.load_state_dict(csd, strict=False) # load
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class SegmentationModel(DetectionModel):
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# YOLOv5 segmentation model
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def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, anchors=None):
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super().__init__(cfg, ch, nc, anchors)
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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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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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# 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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ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak
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csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
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csd = intersect_state_dicts(csd, self.state_dict()) # intersect
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self.load_state_dict(csd, strict=False) # load
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