import torch import numpy as np from tqdm import tqdm import json def load_data(file_name: str = "./uvr5_pack/name_params.json") -> dict: with open(file_name, "r") as f: data = json.load(f) return data def make_padding(width, cropsize, offset): left = offset roi_size = cropsize - left * 2 if roi_size == 0: roi_size = cropsize right = roi_size - (width % roi_size) + left return left, right, roi_size def inference(X_spec, device, model, aggressiveness, data): """ data : dic configs """ def _execute( X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True ): model.eval() with torch.no_grad(): preds = [] iterations = [n_window] total_iterations = sum(iterations) for i in tqdm(range(n_window)): start = i * roi_size X_mag_window = X_mag_pad[ None, :, :, start : start + data["window_size"] ] X_mag_window = torch.from_numpy(X_mag_window) if is_half: X_mag_window = X_mag_window.half() X_mag_window = X_mag_window.to(device) pred = model.predict(X_mag_window, aggressiveness) pred = pred.detach().cpu().numpy() preds.append(pred[0]) pred = np.concatenate(preds, axis=2) return pred def preprocess(X_spec): X_mag = np.abs(X_spec) X_phase = np.angle(X_spec) return X_mag, X_phase X_mag, X_phase = preprocess(X_spec) coef = X_mag.max() X_mag_pre = X_mag / coef n_frame = X_mag_pre.shape[2] pad_l, pad_r, roi_size = make_padding(n_frame, data["window_size"], model.offset) n_window = int(np.ceil(n_frame / roi_size)) X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant") if list(model.state_dict().values())[0].dtype == torch.float16: is_half = True else: is_half = False pred = _execute( X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half ) pred = pred[:, :, :n_frame] if data["tta"]: pad_l += roi_size // 2 pad_r += roi_size // 2 n_window += 1 X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant") pred_tta = _execute( X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half ) pred_tta = pred_tta[:, :, roi_size // 2 :] pred_tta = pred_tta[:, :, :n_frame] return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase) else: return pred * coef, X_mag, np.exp(1.0j * X_phase) def _get_name_params(model_path, model_hash): data = load_data() flag = False ModelName = model_path for type in list(data): for model in list(data[type][0]): for i in range(len(data[type][0][model])): if str(data[type][0][model][i]["hash_name"]) == model_hash: flag = True elif str(data[type][0][model][i]["hash_name"]) in ModelName: flag = True if flag: model_params_auto = data[type][0][model][i]["model_params"] param_name_auto = data[type][0][model][i]["param_name"] if type == "equivalent": return param_name_auto, model_params_auto else: flag = False return param_name_auto, model_params_auto