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