Merge pull request #12 from shahules786/dev-reformat

Dev reformat

.flake8

@@ -1,4 +1,5 @@
 [flake8]
+per-file-ignores = __init__.py:F401
 ignore = E203, E266, E501, W503
 # line length is intentionally set to 80 here because black uses Bugbear
 # See https://github.com/psf/black/blob/master/README.md#line-length for more details

enhancer/data/fileprocessor.py

@@ -1,6 +1,5 @@
 import glob
 import os
-from re import S
 import numpy as np
 from scipy.io import wavfile
 

enhancer/loss.py

@@ -17,8 +17,8 @@ class mean_squared_error(nn.Module):
 
         if prediction.size() != target.size() or target.ndim < 3:
             raise TypeError(
-                f"""Inputs must be of the same shape (batch_size,channels,samples) 
-                            got {prediction.size()} and {target.size()} instead"""
+                f"""Inputs must be of the same shape (batch_size,channels,samples)
+                    got {prediction.size()} and {target.size()} instead"""
             )
 
         return self.loss_fun(prediction, target)
@@ -39,7 +39,7 @@ class mean_absolute_error(nn.Module):
 
         if prediction.size() != target.size() or target.ndim < 3:
             raise TypeError(
-                f"""Inputs must be of the same shape (batch_size,channels,samples) 
+                f"""Inputs must be of the same shape (batch_size,channels,samples)
                             got {prediction.size()} and {target.size()} instead"""
             )
 
@@ -65,7 +65,7 @@ class Si_SDR(nn.Module):
 
         if prediction.size() != target.size() or target.ndim < 3:
             raise TypeError(
-                f"""Inputs must be of the same shape (batch_size,channels,samples) 
+                f"""Inputs must be of the same shape (batch_size,channels,samples)
                             got {prediction.size()} and {target.size()} instead"""
             )
 
@@ -119,7 +119,8 @@ class Avergeloss(nn.Module):
     def validate_loss(self, loss: str):
         if loss not in LOSS_MAP.keys():
             raise ValueError(
-                f"Invalid loss function {loss}, available loss functions are {tuple([loss for loss in LOSS_MAP.keys()])}"
+                f"""Invalid loss function {loss}, available loss functions are
+                    {tuple([loss for loss in LOSS_MAP.keys()])}"""
             )
         else:
             return LOSS_MAP[loss]

enhancer/models/model.py

@@ -1,16 +1,10 @@
-try:
-    from functools import cached_property
-except ImportError:
-    from backports.cached_property import cached_property
 from importlib import import_module
 from huggingface_hub import cached_download, hf_hub_url
-import logging
 import numpy as np
 import os
 from typing import Optional, Union, List, Text, Dict, Any
 from torch.optim import Adam
 import torch
-from torch.nn.functional import pad
 import pytorch_lightning as pl
 from pytorch_lightning.utilities.cloud_io import load as pl_load
 from urllib.parse import urlparse
@@ -19,7 +13,6 @@ from pathlib import Path
 
 from enhancer import __version__
 from enhancer.data.dataset import EnhancerDataset
-from enhancer.utils.io import Audio
 from enhancer.loss import Avergeloss
 from enhancer.inference import Inference
 
@@ -300,7 +293,7 @@ class Model(pl.LightningModule):
 
         with torch.no_grad():
             for batch_id in range(0, batch.shape[0], batch_size):
-                batch_data = batch[batch_id : batch_id + batch_size, :, :].to(
+                batch_data = batch[batch_id: batch_id + batch_size, :, :].to(
                     self.device
                 )
                 prediction = self(batch_data)

enhancer/utils/utils.py

@@ -19,6 +19,7 @@ def check_files(root_dir: str, files: Files):
 
 
 def merge_dict(default_dict: dict, custom: Optional[dict] = None):
+
     params = dict(default_dict)
     if custom:
         params.update(custom)