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Python resnet_weights_helper.load_pretrained_imagenet_weights方法代碼示例

本文整理匯總了Python中utils.resnet_weights_helper.load_pretrained_imagenet_weights方法的典型用法代碼示例。如果您正苦於以下問題：Python resnet_weights_helper.load_pretrained_imagenet_weights方法的具體用法？Python resnet_weights_helper.load_pretrained_imagenet_weights怎麽用？Python resnet_weights_helper.load_pretrained_imagenet_weights使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類utils.resnet_weights_helper的用法示例。


在下文中一共展示了resnet_weights_helper.load_pretrained_imagenet_weights方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: _init_modules

# 需要導入模塊: from utils import resnet_weights_helper [as 別名]
# 或者: from utils.resnet_weights_helper import load_pretrained_imagenet_weights [as 別名]
def _init_modules(self):
        if cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS:
            resnet_utils.load_pretrained_imagenet_weights(self)
            # Check if shared weights are equaled
            if cfg.MODEL.MASK_ON and getattr(self.Mask_Head, 'SHARE_RES5', False):
                assert compare_state_dict(self.Mask_Head.res5.state_dict(), self.Box_Head.res5.state_dict())
            if cfg.MODEL.KEYPOINTS_ON and getattr(self.Keypoint_Head, 'SHARE_RES5', False):
                assert compare_state_dict(self.Keypoint_Head.res5.state_dict(), self.Box_Head.res5.state_dict())

        if cfg.TRAIN.FREEZE_CONV_BODY:
            for p in self.Conv_Body.parameters():
                p.requires_grad = False

        if cfg.TRAIN.FREEZE_RPN_BODY:
            for p in self.RPN.parameters():
                p.requires_grad = False

        if cfg.TRAIN.FREEZE_FASTER_RCNN:
            for p in self.Box_Head.parameters():
                p.requires_grad = False
            for p in self.Box_Outs.parameters():
                p.requires_grad = False
開發者ID:bobwan1995，項目名稱:PMFNet，代碼行數:24，代碼來源:model_builder.py

示例2: _init_modules

# 需要導入模塊: from utils import resnet_weights_helper [as 別名]
# 或者: from utils.resnet_weights_helper import load_pretrained_imagenet_weights [as 別名]
def _init_modules(self):
        if cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS:
            resnet_utils.load_pretrained_imagenet_weights(self)
            # Check if shared weights are equaled
            if cfg.MODEL.MASK_ON and getattr(self.Mask_Head, 'SHARE_RES5', False):
                assert compare_state_dict(self.Mask_Head.res5.state_dict(), self.Box_Head.res5.state_dict())
            if cfg.MODEL.KEYPOINTS_ON and getattr(self.Keypoint_Head, 'SHARE_RES5', False):
                assert compare_state_dict(self.Keypoint_Head.res5.state_dict(), self.Box_Head.res5.state_dict())

        if cfg.TRAIN.FREEZE_CONV_BODY:
            for p in self.Conv_Body.parameters():
                p.requires_grad = False
開發者ID:roytseng-tw，項目名稱:Detectron.pytorch，代碼行數:14，代碼來源:model_builder.py

示例3: _init_modules

# 需要導入模塊: from utils import resnet_weights_helper [as 別名]
# 或者: from utils.resnet_weights_helper import load_pretrained_imagenet_weights [as 別名]
def _init_modules(self):
        if cfg.RESNETS.IMAGENET_PRETRAINED_WEIGHTS != '':  # or cfg.MODEL.USE_SE_LOSS:
            logger.info("Loading pretrained weights from %s", cfg.RESNETS.IMAGENET_PRETRAINED_WEIGHTS)
            resnet_utils.load_pretrained_imagenet_weights(self)
        # Check if shared weights are equaled
        if cfg.MODEL.MASK_ON and getattr(self.Mask_Head, 'SHARE_RES5', False):
            assert self.Mask_Head.res5.state_dict() == self.Box_Head.res5.state_dict()
        if cfg.MODEL.KEYPOINTS_ON and getattr(self.Keypoint_Head, 'SHARE_RES5', False):
            assert self.Keypoint_Head.res5.state_dict() == self.Box_Head.res5.state_dict()
        
        # load detectron pretrained weights for resnet
        if cfg.RESNETS.COCO_PRETRAINED_WEIGHTS != '':
            logger.info("loading detectron pretrained weights from %s", cfg.RESNETS.COCO_PRETRAINED_WEIGHTS)
            load_detectron_weight(self, cfg.RESNETS.COCO_PRETRAINED_WEIGHTS, ('cls_score', 'bbox_pred'))

        if cfg.VGG16.COCO_PRETRAINED_WEIGHTS != '':
            logger.info("loading pretrained weights from %s", cfg.VGG16.COCO_PRETRAINED_WEIGHTS)
            checkpoint = torch.load(cfg.VGG16.COCO_PRETRAINED_WEIGHTS, map_location=lambda storage, loc: storage)
            # not using the last softmax layers
            del checkpoint['model']['Box_Outs.cls_score.weight']
            del checkpoint['model']['Box_Outs.cls_score.bias']
            del checkpoint['model']['Box_Outs.bbox_pred.weight']
            del checkpoint['model']['Box_Outs.bbox_pred.bias']
            net_utils.load_ckpt(self, checkpoint['model'])
            
        if cfg.RESNETS.TO_BE_FINETUNED_WEIGHTS != '':
            logger.info("loading trained and to be finetuned weights from %s", cfg.RESNETS.TO_BE_FINETUNED_WEIGHTS)
            checkpoint = torch.load(cfg.RESNETS.TO_BE_FINETUNED_WEIGHTS, map_location=lambda storage, loc: storage)
            net_utils.load_ckpt(self, checkpoint['model'])

        if cfg.TRAIN.FREEZE_CONV_BODY:
            for p in self.Conv_Body.parameters():
                p.requires_grad = False
開發者ID:jz462，項目名稱:Large-Scale-VRD.pytorch，代碼行數:35，代碼來源:model_builder.py

示例4: _init_modules

# 需要導入模塊: from utils import resnet_weights_helper [as 別名]
# 或者: from utils.resnet_weights_helper import load_pretrained_imagenet_weights [as 別名]
def _init_modules(self):
        # VGG16 imagenet pretrained model is initialized in VGG16.py
        if cfg.RESNETS.IMAGENET_PRETRAINED_WEIGHTS != '':
            logger.info("Loading pretrained weights from %s", cfg.RESNETS.IMAGENET_PRETRAINED_WEIGHTS)
            resnet_utils.load_pretrained_imagenet_weights(self)
                
        if cfg.RESNETS.VRD_PRETRAINED_WEIGHTS != '':
            self.load_detector_weights(cfg.RESNETS.VRD_PRETRAINED_WEIGHTS)
        if cfg.VGG16.VRD_PRETRAINED_WEIGHTS != '':
            self.load_detector_weights(cfg.VGG16.VRD_PRETRAINED_WEIGHTS)
            
        if cfg.RESNETS.VG_PRETRAINED_WEIGHTS != '':
            self.load_detector_weights(cfg.RESNETS.VG_PRETRAINED_WEIGHTS)
        if cfg.VGG16.VG_PRETRAINED_WEIGHTS != '':
            self.load_detector_weights(cfg.VGG16.VG_PRETRAINED_WEIGHTS)

        if cfg.TRAIN.FREEZE_CONV_BODY:
            for p in self.Conv_Body.parameters():
                p.requires_grad = False

        if cfg.RESNETS.VRD_PRD_PRETRAINED_WEIGHTS != '' or cfg.VGG16.VRD_PRD_PRETRAINED_WEIGHTS != '' or \
            cfg.RESNETS.VG_PRD_PRETRAINED_WEIGHTS != '' or cfg.VGG16.VG_PRD_PRETRAINED_WEIGHTS != '':
            if cfg.RESNETS.VRD_PRD_PRETRAINED_WEIGHTS != '':
                logger.info("loading prd pretrained weights from %s", cfg.RESNETS.VRD_PRD_PRETRAINED_WEIGHTS)
                checkpoint = torch.load(cfg.RESNETS.VRD_PRD_PRETRAINED_WEIGHTS, map_location=lambda storage, loc: storage)
            if cfg.VGG16.VRD_PRD_PRETRAINED_WEIGHTS != '':
                logger.info("loading prd pretrained weights from %s", cfg.VGG16.VRD_PRD_PRETRAINED_WEIGHTS)
                checkpoint = torch.load(cfg.VGG16.VRD_PRD_PRETRAINED_WEIGHTS, map_location=lambda storage, loc: storage)
            if cfg.RESNETS.VG_PRD_PRETRAINED_WEIGHTS != '':
                logger.info("loading prd pretrained weights from %s", cfg.RESNETS.VG_PRD_PRETRAINED_WEIGHTS)
                checkpoint = torch.load(cfg.RESNETS.VG_PRD_PRETRAINED_WEIGHTS, map_location=lambda storage, loc: storage)
            if cfg.VGG16.VG_PRD_PRETRAINED_WEIGHTS != '':
                logger.info("loading prd pretrained weights from %s", cfg.VGG16.VG_PRD_PRETRAINED_WEIGHTS)
                checkpoint = torch.load(cfg.VGG16.VG_PRD_PRETRAINED_WEIGHTS, map_location=lambda storage, loc: storage)
            # not using the last softmax layers
            del checkpoint['model']['Box_Outs.cls_score.weight']
            del checkpoint['model']['Box_Outs.cls_score.bias']
            del checkpoint['model']['Box_Outs.bbox_pred.weight']
            del checkpoint['model']['Box_Outs.bbox_pred.bias']
            net_utils.load_ckpt(self.Prd_RCNN, checkpoint['model'])
            if cfg.TRAIN.FREEZE_PRD_CONV_BODY:
                for p in self.Prd_RCNN.Conv_Body.parameters():
                    p.requires_grad = False
            if cfg.TRAIN.FREEZE_PRD_BOX_HEAD:
                for p in self.Prd_RCNN.Box_Head.parameters():
                    p.requires_grad = False
開發者ID:jz462，項目名稱:Large-Scale-VRD.pytorch，代碼行數:48，代碼來源:model_builder_rel.py


注：本文中的utils.resnet_weights_helper.load_pretrained_imagenet_weights方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。