数据集下载和解压代码 ( 部分封装的方法请参考 GitHub完整代码 )：
string url = "https://github.com/SciSharp/SciSharp-Stack-Examples/blob/master/data/data_CnnInYourOwnData.zip"; Directory.CreateDirectory(Name); Utility.Web.Download(url, Name, "data_CnnInYourOwnData.zip"); Utility.Compress.UnZip(Name + "data_CnnInYourOwnData.zip", Name);
字典创建
读取目录下的子文件夹名称，作为分类的字典，方便后面One-hot使用
private void FillDictionaryLabel(string DirPath)
{
string[] str_dir = Directory.GetDirectories(DirPath, "*", SearchOption.TopDirectoryOnly);
int str_dir_num = str_dir.Length;
if (str_dir_num > 0)
{
Dict_Label = new Dictionary<Int64, string>();
for (int i = 0; i < str_dir_num; i++)
{
string label = (str_dir[i].Replace(DirPath + "", "")).Split('').First();
Dict_Label.Add(i, label);
print(i.ToString() + " : " + label);
}
n_classes = Dict_Label.Count;
}
}
文件List读取和打乱
从文件夹中读取train、validation、test的list，并随机打乱顺序。
读取目录
ArrayFileName_Train = Directory.GetFiles(Name + "train", "*.*", SearchOption.AllDirectories); ArrayLabel_Train = GetLabelArray(ArrayFileName_Train); ArrayFileName_Validation = Directory.GetFiles(Name + "validation", "*.*", SearchOption.AllDirectories); ArrayLabel_Validation = GetLabelArray(ArrayFileName_Validation); ArrayFileName_Test = Directory.GetFiles(Name + "test", "*.*", SearchOption.AllDirectories); ArrayLabel_Test = GetLabelArray(ArrayFileName_Test);
获得标签
private Int64[] GetLabelArray(string[] FilesArray)
{
Int64[] ArrayLabel = new Int64[FilesArray.Length];
for (int i = 0; i < ArrayLabel.Length; i++)
{
string[] labels = FilesArray[i].Split('');
string label = labels[labels.Length - 2];
ArrayLabel[i] = Dict_Label.Single(k => k.Value == label).Key;
}
return ArrayLabel;
}
随机乱序
public (string[], Int64[]) ShuffleArray(int count, string[] images, Int64[] labels)
{
ArrayList mylist = new ArrayList();
string[] new_images = new string[count];
Int64[] new_labels = new Int64[count];
Random r = new Random();
for (int i = 0; i < count; i++)
{
mylist.Add(i);
}
for (int i = 0; i < count; i++)
{
int rand = r.Next(mylist.Count);
new_images[i] = images[(int)(mylist[rand])];
new_labels[i] = labels[(int)(mylist[rand])];
mylist.RemoveAt(rand);
}
print("shuffle array list： " + count.ToString());
return (new_images, new_labels);
}
部分数据集预先载入
Validation/Test数据集和标签一次性预先载入成NDArray格式。
private void LoadImagesToNDArray()
{
//Load labels
y_valid = np.eye(Dict_Label.Count)[new NDArray(ArrayLabel_Validation)];
y_test = np.eye(Dict_Label.Count)[new NDArray(ArrayLabel_Test)];
print("Load Labels To NDArray : OK!");
//Load Images
x_valid = np.zeros(ArrayFileName_Validation.Length, img_h, img_w, n_channels);
x_test = np.zeros(ArrayFileName_Test.Length, img_h, img_w, n_channels);
LoadImage(ArrayFileName_Validation, x_valid, "validation");
LoadImage(ArrayFileName_Test, x_test, "test");
print("Load Images To NDArray : OK!");
}
private void LoadImage(string[] a, NDArray b, string c)
{
for (int i = 0; i < a.Length; i++)
{
b[i] = ReadTensorFromImageFile(a[i]);
Console.Write(".");
}
Console.WriteLine();
Console.WriteLine("Load Images To NDArray: " + c);
}
private NDArray ReadTensorFromImageFile(string file_name)
{
using (var graph = tf.Graph().as_default())
{
var file_reader = tf.read_file(file_name, "file_reader");
var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: n_channels, name: "DecodeJpeg");
var cast = tf.cast(decodeJpeg, tf.float32);
var dims_expander = tf.expand_dims(cast, 0);
var resize = tf.constant(new int[] { img_h, img_w });
var bilinear = tf.image.resize_bilinear(dims_expander, resize);
var sub = tf.subtract(bilinear, new float[] { img_mean });
var normalized = tf.divide(sub, new float[] { img_std });
using (var sess = tf.Session(graph))
{
return sess.run(normalized);
}
}
}
