def _resnet(arch, block, layers, pretrained, progress, **kwargs): model = ResNet(block, layers, **kwargs) if pretrained: state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) load_pretrained_params(model, state_dict) return model
def l_resnext101(pretrained, baseWidth, cardinality): """ Construct ResNeXt-101. """ model = ResNeXt(baseWidth, cardinality, [3, 4, 23, 3], 1000) if pretrained: pretrained_dict = model_zoo.load_url(model_urls["resnext101"]) load_pretrained_params(model, pretrained_dict) return model
def l_resnet101(pretrained=False, **kwargs): """Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) if pretrained: pretrained_dict = model_zoo.load_url(model_urls["resnet101"]) load_pretrained_params(model, pretrained_dict) return model
def _load_model(backbone, pretrained, progress, in_channel, num_classes, replace_stride_with_dilation): model = _segm_resnet(backbone, in_channel, num_classes, replace_stride_with_dilation) if pretrained: arch = "deeplabv3_" + backbone + "_coco" model_url = model_urls[arch] if model_url is None: raise NotImplementedError( "pretrained {} is not supported as of now".format(arch)) else: print(f"Loading parameters from {model_url}") state_dict = load_state_dict_from_url(model_url, progress=progress) load_pretrained_params(model, state_dict) return model
def Res_Deeplab(pretrained: bool = True) -> nn.Module: model = ResNet(Bottleneck, [3, 4, 23, 3]) if pretrained: pretrained_dict = torch.load(model_urls["res_deeplabv3"]) load_pretrained_params(model, pretrained_dict["model"]) return model