call_hook('before_run') # _init_model_weights self._init_model_weights() # 如果有checkpoints,则加载 modules = self.cfg.get('activation_checkpointing', None) # 如果加载了checkpoints,打印信息 if modules is not None: self.logger.info(f'Enabling the "activation_checkpointing" feature for ...
sample_offest偏移量(thetas ,多头不同的方向): def init_weights(self) -> None: """Default initialization for Parameters of Module.""" constant_init(self.sampling_offsets, 0.) device = next(self.parameters()).device thetas = torch.arange( self.num_heads, dtype=torch.float32, device=devic...
self.init_weights(cfg.pretrained) 调用模型 cfg = mmcv.Config.fromfile('config.py') model = builder.build_model(cfg.model) load_checkpoint(model, 'checkpoint.pth') 在上述示例中,我们首先自定义了一个名为MySegModel的分割模型,然后通过装饰器@MODELS.register_module()将其注册为MySegModelWrapper。最...
(m, std=0.001) normal_init(self.cls_prob_module[-1], std=0.01, bias=bias_cls) self.cls_decomp.init_weights() self.reg_decomp.init_weights() normal_init(self.tood_cls, std=0.01, bias=bias_cls) normal_init(self.tood_reg, std=0.01) def forward(self, feats): """Forward features ...
net.apply(init_weights) net = net.to(device) criterion = nn.CrossEntropyLoss() #交叉熵描述了两个概率分布之间的距离,交叉熵越小说明两者之间越接近 train_ch5(net, train_iter, test_iter, criterion,num_epochs, batch_size,device, lr)
If left as None, the model will not load any weights. Returns: nn.Module: The constructed detector. """ if isinstance(config, str): config = mmcv.Config.fromfile(config) elif not isinstance(config, mmcv.Config): raise TypeError('config must be a filename or Config object, ' 'but got...
create model and load weights model = init_detector(config_file, checkpoint_file, device='cpu') init visualizer visualizer = VISUALIZERS.build(model.cfg.visualizer) the dataset_meta is loaded from the checkpoint and then pass to the model in init_detector ...
model = nn.Sequential( # 使用神经网络层序列定义我们的模型 nn.Linear(D_in, H), # 每一层都包含 learnable weights,它的形状为:D_in * H nn.ReLU(), nn.Linear(H, D_out) # w2 的形状为 H * D_out ) learning_rate = 1e-2
特别的,mmdet3.x引入了visualizer, 使用前需要配合模型先注册再使用(model.cfg.visualizer), 且对于一个模型,只要注册一次就行了,注册多个visualizer实例会有报错 from mmdet.registry import VISUALIZERS # Init visualizer visualizer = VISUALIZERS.build(model.cfg.visualizer) # The dataset_meta is loaded from ...
(1, 2, 3), with_cp=False, convert_weights=True, init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)), neck=dict( type='YOLOv5PAFPN', deepen_factor=deepen_factor, widen_factor=widen_factor, in_channels=channels, # 注意:SwinTransformer-Tiny 输出的3个通道是 [192, 384, 768],...