def model_fn(model_dir): """Load the PyTorch model from the `model_dir` directory.""" print("Loading model.") model_info = {} model_info_path = os.path.join(model_dir, 'model_info.pth') with open(model_info_path, 'rb') as f: model_info = torch.load(f) print("model_info: {}".format(model_info)) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = BinaryClassifier(model_info['input_features'], model_info['hidden_dim'], model_info['output_dim']) model_path = os.path.join(model_dir, 'model.pth') with open(model_path, 'rb') as f: model.load_state_dict(torch.load(f)) model.to(device).eval() print("Done loading model.") return model
def model_fn(model_dir): """Load the PyTorch model from the `model_dir` directory.""" print("Loading model.") # First, load the parameters used to create the model. model_info = {} model_info_path = os.path.join(model_dir, 'model_info.pth') with open(model_info_path, 'rb') as f: model_info = torch.load(f) print("model_info: {}".format(model_info)) # Determine the device and construct the model. device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = BinaryClassifier(model_info['input_features'], model_info['hidden_dim'], model_info['output_dim']) # Load the stored model parameters. model_path = os.path.join(model_dir, 'model.pth') with open(model_path, 'rb') as f: model.load_state_dict(torch.load(f)) # set to eval mode, could use no_grad model.to(device).eval() print("Done loading model.") return model
def model_fn(model_dir): """Load the PyTorch model from the `model_dir` directory.""" print("Loading model.") # First, load the parameters used to create the model. model_info = {} model_info_path = os.path.join(model_dir, "model_info.pth") with open(model_info_path, "rb") as f: model_info = torch.load(f) print("model_info: {}".format(model_info)) # Determine the device and construct the model. device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = BinaryClassifier( model_info["input_features"], model_info["hidden_dim"], model_info["output_dim"], model_info["momentum"], model_info["dropout_rate"], model_info["num_layers"], ) # Load the store model parameters. model_path = os.path.join(model_dir, "model.pth") with open(model_path, "rb") as f: model.load_state_dict(torch.load(f)) # Prep for testing model.to(device).eval() print("Done loading model.") return model