From 779e1eb1161b8c9af9df7f87c83bcc461a8c3f77 Mon Sep 17 00:00:00 2001
From: Anjok07 <68268275+Anjok07@users.noreply.github.com>
Date: Mon, 9 Nov 2020 04:27:44 -0600
Subject: [PATCH] Delete dataset.py

---
 lib/dataset.py | 119 -------------------------------------------------
 1 file changed, 119 deletions(-)
 delete mode 100644 lib/dataset.py

diff --git a/lib/dataset.py b/lib/dataset.py
deleted file mode 100644
index b89c016..0000000
--- a/lib/dataset.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import os
-
-import numpy as np
-import torch
-from tqdm import tqdm
-
-from lib import spec_utils
-
-
-class VocalRemoverValidationSet(torch.utils.data.Dataset):
-
-    def __init__(self, filelist):
-        self.filelist = filelist
-
-    def __len__(self):
-        return len(self.filelist)
-
-    def __getitem__(self, idx):
-        path = self.filelist[idx]
-        data = np.load(path)
-
-        return data['X'], data['y']
-
-
-def mixup_generator(X, y, rate, alpha):
-    perm = np.random.permutation(len(X))[:int(len(X) * rate)]
-    for i in range(len(perm) - 1):
-        lam = np.random.beta(alpha, alpha)
-        X[perm[i]] = lam * X[perm[i]] + (1 - lam) * X[perm[i + 1]]
-        y[perm[i]] = lam * y[perm[i]] + (1 - lam) * y[perm[i + 1]]
-
-    return X, y
-
-
-def get_oracle_data(X, y, instance_loss, oracle_rate, oracle_drop_rate):
-    k = int(len(X) * oracle_rate * (1 / (1 - oracle_drop_rate)))
-    n = int(len(X) * oracle_rate)
-    idx = np.argsort(instance_loss)[::-1][:k]
-    idx = np.random.choice(idx, n, replace=False)
-    oracle_X = X[idx].copy()
-    oracle_y = y[idx].copy()
-
-    return oracle_X, oracle_y, idx
-
-
-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 make_training_set(filelist, cropsize, patches, sr, hop_length, offset):
-    len_dataset = patches * len(filelist)
-    X_dataset = np.zeros(
-        (len_dataset, 2, hop_length, cropsize), dtype=np.float32)
-    y_dataset = np.zeros(
-        (len_dataset, 2, hop_length, cropsize), dtype=np.float32)
-    for i, (X_path, y_path) in enumerate(tqdm(filelist)):
-        p = np.random.uniform()
-        if p < 0.1:
-            X_path.replace(os.path.splitext(X_path)[1], '_pitch-1.wav')
-            y_path.replace(os.path.splitext(y_path)[1], '_pitch-1.wav')
-        elif p < 0.2:
-            X_path.replace(os.path.splitext(X_path)[1], '_pitch1.wav')
-            y_path.replace(os.path.splitext(y_path)[1], '_pitch1.wav')
-
-        X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length)
-        coeff = np.max([X.max(), y.max()])
-        X, y = X / coeff, y / coeff
-
-        l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
-        X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode='constant')
-        y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode='constant')
-
-        starts = np.random.randint(0, X_pad.shape[2] - cropsize, patches)
-        ends = starts + cropsize
-        for j in range(patches):
-            idx = i * patches + j
-            X_dataset[idx] = X_pad[:, :, starts[j]:ends[j]]
-            y_dataset[idx] = y_pad[:, :, starts[j]:ends[j]]
-            if np.random.uniform() < 0.5:
-                # swap channel
-                X_dataset[idx] = X_dataset[idx, ::-1]
-                y_dataset[idx] = y_dataset[idx, ::-1]
-
-    return X_dataset, y_dataset
-
-
-def make_validation_set(filelist, cropsize, sr, hop_length, offset):
-    patch_list = []
-    outdir = 'cs{}_sr{}_hl{}_of{}'.format(cropsize, sr, hop_length, offset)
-    os.makedirs(outdir, exist_ok=True)
-    for i, (X_path, y_path) in enumerate(tqdm(filelist)):
-        basename = os.path.splitext(os.path.basename(X_path))[0]
-
-        X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length)
-        coeff = np.max([X.max(), y.max()])
-        X, y = X / coeff, y / coeff
-
-        l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
-        X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode='constant')
-        y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode='constant')
-
-        len_dataset = int(np.ceil(X.shape[2] / roi_size))
-        for j in range(len_dataset):
-            outpath = os.path.join(outdir, '{}_p{}.npz'.format(basename, j))
-            start = j * roi_size
-            if not os.path.exists(outpath):
-                np.savez(
-                    outpath,
-                    X=X_pad[:, :, start:start + cropsize],
-                    y=y_pad[:, :, start:start + cropsize])
-            patch_list.append(outpath)
-
-    return VocalRemoverValidationSet(patch_list)