'update'

model/another-try/Wave-U-Net/.idea/.gitignore

@@ -1,3 +0,0 @@
-# Default ignored files
-/shelf/
-/workspace.xml

model/another-try/Wave-U-Net/.idea/Wave-U-Net.iml

@@ -1,10 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$">
-      <excludeFolder url="file://$MODULE_DIR$/venv" />
-    </content>
-    <orderEntry type="jdk" jdkName="Python 3.8 (base)" jdkType="Python SDK" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-</module>

model/another-try/Wave-U-Net/.idea/inspectionProfiles/Project_Default.xml

@@ -1,24 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <profile version="1.0">
-    <option name="myName" value="Project Default" />
-    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
-      <option name="ignoredPackages">
-        <value>
-          <list size="11">
-            <item index="0" class="java.lang.String" itemvalue="scikit-image" />
-            <item index="1" class="java.lang.String" itemvalue="scipy" />
-            <item index="2" class="java.lang.String" itemvalue="python" />
-            <item index="3" class="java.lang.String" itemvalue="natsort" />
-            <item index="4" class="java.lang.String" itemvalue="tensorboardx" />
-            <item index="5" class="java.lang.String" itemvalue="pillow" />
-            <item index="6" class="java.lang.String" itemvalue="sklearn" />
-            <item index="7" class="java.lang.String" itemvalue="torch" />
-            <item index="8" class="java.lang.String" itemvalue="numpy" />
-            <item index="9" class="java.lang.String" itemvalue="torchvision" />
-            <item index="10" class="java.lang.String" itemvalue="torchsummary" />
-          </list>
-        </value>
-      </option>
-    </inspection_tool>
-  </profile>
-</component>

model/another-try/Wave-U-Net/.idea/inspectionProfiles/profiles_settings.xml

@@ -1,6 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="USE_PROJECT_PROFILE" value="false" />
-    <version value="1.0" />
-  </settings>
-</component>

model/another-try/Wave-U-Net/.idea/misc.xml

@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (base)" project-jdk-type="Python SDK" />
-</project>

model/another-try/Wave-U-Net/.idea/modules.xml

@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/Wave-U-Net.iml" filepath="$PROJECT_DIR$/.idea/Wave-U-Net.iml" />
-    </modules>
-  </component>
-</project>

model/another-try/Wave-U-Net/.idea/vcs.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$/.." vcs="Git" />
-  </component>
-</project>

model/another-try/Wave-U-Net/main.py

@@ -1,16 +0,0 @@
-# This is a sample Python script.
-
-# Press Shift+F10 to execute it or replace it with your code.
-# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
-
-
-def print_hi(name):
-    # Use a breakpoint in the code line below to debug your script.
-    print(f'Hi, {name}')  # Press Ctrl+F8 to toggle the breakpoint.
-
-
-# Press the green button in the gutter to run the script.
-if __name__ == '__main__':
-    print_hi('PyCharm')
-
-# See PyCharm help at https://www.jetbrains.com/help/pycharm/

model/another-try/Wave-U-Net/model/Conv.py

@@ -1,28 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-
-class ConvLayer(nn.Module):
-    def __init__(self, input, output, kernel_size, stride, transpose=False):
-        super(ConvLayer,self).__init__()
-        self.input = input
-        self.output = output
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.transpose = transpose
-
-        if self.transpose:
-            self.conv = nn.ConvTranspose1d(input, output, kernel_size=self.kernel_size,
-                                           stride=self.stride,padding=self.kernel_size - 1)
-        else:
-            self.conv = nn.Conv1d(input, output, kernel_size=self.kernel_size, stride=self.stride)
-
-        self.norm = nn.GroupNorm(output // 8, output)
-
-
-    def forward(self,x):
-        x = self.conv(x)
-        x = self.norm(x)
-        output = nn.ReLU(x)
-
-        return output
-

model/another-try/Wave-U-Net/model/DownSampling.py

@@ -1,15 +0,0 @@
-import torch
-import torch.nn as nn
-from model.Conv import ConvLayer
-from model.Resample import Resample
-
-class DownsamplingBlock(nn.Module):
-    def __init__(self, stride, kernel_size, padding):
-        super(DownsamplingBlock, self).__init__()
-        self.stride = stride
-        self.kernel_size = kernel_size
-
-    def forward(self,x):
-
-
-        return out

model/another-try/Wave-U-Net/model/Resample.py

@@ -1,17 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-
-class Resample(nn.Module):
-    def __init__(self, channels, kernel_size, stride, padding):
-        super(Resample,self).__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-
-
-    def forward(self, x):
-
-        out = x
-
-        return out

model/another-try/Wave-U-Net/model/UpSampling.py

@@ -1,17 +0,0 @@
-import torch
-import torch.nn as nn
-from model.Conv import ConvLayer
-from model.Resample import Resample
-
-class UpsamplingBlock(nn.Module):
-    def __init__(self, stride, kernel_size, padding):
-        super(Upsampling,self).__init__()
-        self.stride = stride
-        self.kernel_size = kernel_size
-        self.padding = padding
-
-
-    def forward(self, x):
-        out = x
-
-        return out

model/another-try/Wave-U-Net/model/test.py

@@ -1,41 +0,0 @@
-import numpy as np
-import torch.nn as nn
-import torch
-
-# k = 3
-#
-# dconv1 = nn.Conv1d(1, 1, kernel_size=k, stride=1, padding=0, bias=False)
-#
-# dconv1.weight.data = torch.ones(1, 1, k)
-#
-# x = torch.ones(1, 1, 4)
-#
-# # print('=====dconv1=====')
-# #
-# # for name, l in dconv1.named_parameters():
-# #     print('{}={}'.format(name, l.data))
-#
-# x3 = dconv1(x)
-#
-# class MyModule(nn.Module):
-#     def __init__(self):
-#         super(MyModule, self).__init__()
-#         self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(10)])
-#
-#     def forward(self, x):
-#         # ModuleList can act as an iterable, or be indexed using ints
-#         for i, l in enumerate(self.linears):
-#             x = self.linears[i // 2](x) + l(x)
-#         return x
-#
-# x = np.random.randint(2, size=(1,2,3,4))
-#
-# z = np.random.randint(2, size=(2,2,2))
-# y = x[0,:,1:3,2:4]
-# print(x)
-# print(y.shape)
-# print(y.dot(z).shape)
-
-from py2neo import Graph,Node,Relationship
-# 连接neo4j数据库，输入地址、用户名、密码
-graph = Graph('http://localhost:7474',auth=("neo4j", "test"))

model/another-try/Wave-U-Net/model/wave-u-net.py

@@ -1,29 +0,0 @@
-import torch
-import torch.nn as nn
-from model.Conv import ConvLayer
-from model.Resample import Resample
-
-class DownsamplingBlock(nn.Module):
-    def __init__(self, stride, kernel_size):
-        super(DownsamplingBlock, self).__init__()
-        self.stride = stride
-        self.kernel_size = kernel_size
-
-    def forward(self,x):
-        out = x
-
-
-        return out
-
-class UpsamplingBlock(nn.Module):
-    def __init__(self, stride, kernel_size, padding):
-        super(Upsampling,self).__init__()
-        self.stride = stride
-        self.kernel_size = kernel_size
-        self.padding = padding
-
-
-    def forward(self, x):
-        out = x
-
-        return out

model/another-try/Wave-U-Net/read_the_sound.py

@@ -1,74 +0,0 @@
-import torch
-import torchaudio
-import matplotlib as plt
-import musdb
-import os
-import numpy as np
-import glob
-import librosa
-import soundfile
-
-
-
-def load(path, sr=22050, mono=True, mode="numpy", offset=0.0, duration=None):
-    y, curr_sr = librosa.load(path, sr=sr, mono=mono, res_type='kaiser_fast', offset=offset, duration=duration)
-
-    if len(y.shape) == 1:
-        # Expand channel dimension
-        y = y[np.newaxis, :]
-
-    if mode == "pytorch":
-        y = torch.tensor(y)
-
-    return y, curr_sr
-
-
-def write_wav(path, audio, sr):
-    soundfile.write(path, audio.T, sr, "PCM_16")
-
-def get_musdbhq(database_path):
-    '''
-    Retrieve audio file paths for MUSDB HQ dataset
-    :param database_path: MUSDB HQ root directory
-    :return: dictionary with train and test keys, each containing list of samples, each sample containing all audio paths
-    '''
-    subsets = list()
-
-    for subset in ["train", "test"]:
-        print("Loading " + subset + " set...")
-        tracks = glob.glob(os.path.join(database_path, subset, "*"))
-        samples = list()
-
-        # Go through tracks
-        for track_folder in sorted(tracks):
-            # Skip track if mixture is already written, assuming this track is done already
-            example = dict()
-            for stem in ["mix", "bass", "drums", "other", "vocals"]:
-                filename = stem if stem != "mix" else "mixture"
-                audio_path = os.path.join(track_folder, filename + ".wav")
-                example[stem] = audio_path
-
-            # Add other instruments to form accompaniment
-            acc_path = os.path.join(track_folder, "accompaniment.wav")
-
-            if not os.path.exists(acc_path):
-                print("Writing accompaniment to " + track_folder)
-                stem_audio = []
-                for stem in ["bass", "drums", "other"]:
-                    audio, sr = load(example[stem], sr=None, mono=False)
-                    stem_audio.append(audio)
-                acc_audio = np.clip(sum(stem_audio), -1.0, 1.0)
-                write_wav(acc_path, acc_audio, sr)
-
-            example["accompaniment"] = acc_path
-
-            samples.append(example)
-
-        subsets.append(samples)
-
-    return subsets
-
-path = "C:/Users/IAN/Desktop/Wave-U-Net/musdb18-hq/"
-
-res = get_musdbhq(path)
-print(res)