def load(self, model, pretrain_file): # pre-trained model loading print('Loading the pretrained model from', pretrain_file) if pretrain_file.endswith('.ckpt'): # checkpoint file in tensorflow checkpoint.load_model(model.transformer, pretrain_file) else: model.transformer.load_state_dict({ key[12:]: value for key, value in torch.load(pretrain_file).items() if key.startswith('transformer') })
def load(self, model_file, pretrain_file): """ load saved model or pretrained transformer (a part of model) """ if model_file: print('Loading the model from', model_file) self.model.load_state_dict(torch.load(model_file, map_location=self.device), strict=False) elif pretrain_file: # use pretrained transformer print('Loading the pretrained model from', pretrain_file) if pretrain_file.endswith('.ckpt'): # checkpoint file in tensorflow checkpoint.load_model(self.model.transformer, pretrain_file) elif pretrain_file.endswith('.pt'): # pretrain model file in pytorch self.model.transformer.load_state_dict({ key[12:]: value for key, value in torch.load(pretrain_file).items() if key.startswith('transformer') }) # load only transformer parts
def load(self, model_file, pretrain_file): """ load saved model or pretrained transformer (a part of model) """ if pretrain_file: # use pretrained transformer if pretrain_file.endswith( '.ckpt'): # checkpoint file in tensorflow checkpoint.load_model(self.model.transformer, pretrain_file) elif pretrain_file.endswith( '.pt'): # pretrain model file in pytorch self.model.transformer.load_state_dict({ key[12:]: value for key, value in torch.load(pretrain_file).items() if key.startswith('transformer') }) # load only transformer parts if model_file: self.model.load_state_dict(torch.load(model_file))
def main(): args = parse_args(parameters, "video detection", "video evaluation parameters") print(args) model, encoder, model_args = load_model(args.model) print("model parameters:") print(model_args) classes = model_args.dataset.classes frames, info = cv.video_capture(args.input) print("Input video", info) scale = args.scale or 1 size = (int(info.size[0] * scale), int(info.size[1] * scale)) evaluate_image = initialise(args.model, model, encoder, size, args.backend) evaluate_video(frames, evaluate_image, size, args, classes=classes, fps=info.fps)
def load(self, model, pretrain_file): # pre-trained model loading print('Loading the pretrained model from', pretrain_file) if pretrain_file.endswith('.ckpt'): # checkpoint file in tensorflow checkpoint.load_model(model.transformer, pretrain_file)