Delete dataset.py

dataset.py

@@ -1,80 +0,0 @@
-from torchvision.datasets import MNIST
-import os
-import numpy as np
-import random
-
-train_dataset = MNIST(os.path.join('./', "MNIST"), train=True, download=True)
-test_dataset = MNIST(os.path.join('./', "MNIST"), train=False, download=True)
-
-
-class MNIST_DS(object):
-
-    def __init__(self, train_dataset, test_dataset):
-        self.__train_labels_idx_map = {}
-        self.__test_labels_idx_map = {}
-
-        self.__train_data = train_dataset.data
-        self.__test_data = test_dataset.data
-        self.__train_labels = train_dataset.targets
-        self.__test_labels = test_dataset.targets
-
-        self.__train_labels_np = self.__train_labels.numpy()
-        self.__train_unique_labels = np.unique(self.__train_labels_np)
-
-        self.__test_labels_np = self.__test_labels.numpy()
-        self.__test_unique_labels = np.unique(self.__test_labels_np)
-
-    def load(self):
-        self.__train_labels_idx_map = {}
-        for label in self.__train_unique_labels:
-            self.__train_labels_idx_map[label] = np.where(self.__train_labels_np == label)[0]
-
-        self.__test_labels_idx_map = {}
-        for label in self.__test_unique_labels:
-            self.__test_labels_idx_map[label] = np.where(self.__test_labels_np == label)[0]
-
-    def getTriplet(self, split="train"):
-        pos_label = 0
-        neg_label = 0
-        label_idx_map = None
-        data = None
-
-        if split == 'train':
-            pos_label = self.__train_unique_labels[random.randint(0, len(self.__train_unique_labels) - 1)]
-            neg_label = pos_label
-            while neg_label is pos_label:
-                neg_label = self.__train_unique_labels[random.randint(0, len(self.__train_unique_labels) - 1)]
-            label_idx_map = self.__train_labels_idx_map
-            data = self.__train_data
-        else:
-            pos_label = self.__test_unique_labels[random.randint(0, len(self.__test_unique_labels) - 1)]
-            neg_label = pos_label
-            while neg_label is pos_label:
-                neg_label = self.__test_unique_labels[random.randint(0, len(self.__test_unique_labels) - 1)]
-            label_idx_map = self.__test_labels_idx_map
-            data = self.__test_data
-
-        pos_label_idx_map = label_idx_map[pos_label]
-        pos_img_anchor_idx = pos_label_idx_map[random.randint(0, len(pos_label_idx_map) - 1)]
-        pos_img_idx = pos_img_anchor_idx
-        while pos_img_idx is pos_img_anchor_idx:
-            pos_img_idx = pos_label_idx_map[random.randint(0, len(pos_label_idx_map) - 1)]
-
-        neg_label_idx_map = label_idx_map[neg_label]
-        neg_img_idx = neg_label_idx_map[random.randint(0, len(neg_label_idx_map) - 1)]
-
-        pos_anchor_img = data[pos_img_anchor_idx].numpy()
-        pos_img = data[pos_img_idx].numpy()
-        neg_img = data[neg_img_idx].numpy()
-
-        return pos_anchor_img, pos_img, neg_img
-
-
-dset_obj = MNIST_DS(train_dataset, test_dataset)
-dset_obj.load()
-train_triplets = []
-pos_anchor_img, pos_img, neg_img = dset_obj.getTriplet()
-train_triplets.append([pos_anchor_img, pos_img, neg_img])
-
-print(train_triplets[0][0])
-