def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, number of classes super(Model, self).__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub self.yaml_file = Path(cfg).name with open(cfg) as f: self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict # Define model if nc and nc != self.yaml['nc']: print('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc)) self.yaml['nc'] = nc # override yaml value self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist, ch_out # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) # Build strides, anchors m = self.model[-1] # Detect() if isinstance(m, Detect): s = 128 # 2x min stride m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward m.anchors /= m.stride.view(-1, 1, 1) check_anchor_order(m) self.stride = m.stride self._initialize_biases() # only run once # print('Strides: %s' % m.stride.tolist()) # Init weights, biases initialize_weights(self) self.info() print('init model')
def model_create(self, cfg): if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub self.yaml_file = Path(cfg).name with open(cfg) as f: self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict self.model, self.save = parse_model(deepcopy(self.yaml), ch=[self.ch ]) # model, savelist, ch_out # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) # Build strides, anchors m = self.model[-1] # Detect() if isinstance(m, Detect): s = 128 # 2x min stride m.stride = torch.tensor([ s / x.shape[-2] for x in self.forward(torch.zeros(1, self.ch, s, s)) ]) # forward m.anchors /= m.stride.view(-1, 1, 1) check_anchor_order(m) self.stride = m.stride self._initialize_biases() # only run once # print('Strides: %s' % m.stride.tolist()) initialize_weights(self)
def __init__(self, opt, hyp, cfg='yolov5s.yaml', ch=3, nc=5, tb_writer=None): # model, input channels, number of classes super(Model, self).__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub self.yaml_file = Path(cfg).name with open(cfg) as f: self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict self.nc = nc self.hyp = hyp self.opt = opt self.tb_writer = tb_writer #init seeds init_seeds(2 + self.opt.global_rank) # Define model if nc and nc != self.yaml['nc']: print('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc)) self.yaml['nc'] = nc # override yaml value self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist, ch_out # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) # Build strides, anchors m = self.model[-1] # Detect() if isinstance(m, Detect): s = 128 # 2x min stride m.stride = torch.tensor([ s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s)) ]) # forward m.anchors /= m.stride.view(-1, 1, 1) check_anchor_order(m) self.stride = m.stride self._initialize_biases() # only run once # print('Strides: %s' % m.stride.tolist()) initialize_weights(self) self.info() self.config() self.run_save() self.load_model() #self.wandb_logging() self.optimizer, self.lr_scheduler = self.configure_optimizers() self.optimizer, self.lr_scheduler = self.optimizer[ 0], self.lr_scheduler[0] self.resume()
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes super(Model, self).__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub self.yaml_file = Path(cfg).name with open(cfg) as f: self.yaml = yaml.load(f, Loader=yaml.SafeLoader) # model dict # Define model ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels if nc and nc != self.yaml['nc']: logger.info( f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") self.yaml['nc'] = nc # override yaml value if anchors: logger.info( f'Overriding model.yaml anchors with anchors={anchors}') self.yaml['anchors'] = round(anchors) # override yaml value self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist self.names = [str(i) for i in range(self.yaml['nc'])] # default names # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) # Build strides, anchors m = self.model[-1] # Detect() if isinstance(m, Detect): s = 256 # 2x min stride m.stride = torch.tensor([ s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s)) ]) # forward m.anchors /= m.stride.view(-1, 1, 1) check_anchor_order(m) self.stride = m.stride self._initialize_biases() # only run once # print('Strides: %s' % m.stride.tolist()) # Init weights, biases initialize_weights(self) self.info() logger.info('')