- cleaned up to keep mini_imagenet_dataloader.py originally, therefor…

…e two functions have to be overwritten :(

few_shot_tests.py

@@ -18,6 +18,7 @@
 import tensorflow as tf
 import argparse
 import numpy as np
+import random, imageio
 
 parser = argparse.ArgumentParser(description='train recurrent net.')
 parser.add_argument('--pretrained_name', dest='pretrained_name',  type=str, default=None)
@@ -72,6 +73,67 @@ def idx_to_big(self, phase, idx):
         one_episode_sample_num = self.num_samples_per_class*self.way_num
         return ((idx+1)*one_episode_sample_num >= len(all_filenames))
 
+    def process_batch(self, input_filename_list, input_label_list, batch_sample_num, reshape_with_one=True, dont_shuffle_batch = False):
+        new_path_list = []
+        new_label_list = []
+        for k in range(batch_sample_num):
+            class_idxs = list(range(0, self.way_num))
+            if not dont_shuffle_batch:
+                random.shuffle(class_idxs)
+            for class_idx in class_idxs:
+                true_idx = class_idx*batch_sample_num + k
+                new_path_list.append(input_filename_list[true_idx])
+                new_label_list.append(input_label_list[true_idx])
+
+        img_list = []
+        for filepath in new_path_list:
+            this_img = imageio.imread(filepath)
+            this_img = this_img / 255.0
+            img_list.append(this_img)
+
+        if reshape_with_one:
+            img_array = np.array(img_list)
+            label_array = self.one_hot(np.array(new_label_list)).reshape([1, self.way_num*batch_sample_num, -1])
+        else:
+            img_array = np.array(img_list)
+            label_array = self.one_hot(np.array(new_label_list)).reshape([self.way_num*batch_sample_num, -1])
+        return img_array, label_array
+
+    def get_batch(self, phase='train', idx=0, dont_shuffle_batch = False):
+        if phase=='train':
+            all_filenames = self.train_filenames
+            labels = self.train_labels 
+        elif phase=='val':
+            all_filenames = self.val_filenames
+            labels = self.val_labels 
+        elif phase=='test':
+            all_filenames = self.test_filenames
+            labels = self.test_labels
+        else:
+            print('Please select vaild phase')
+
+        one_episode_sample_num = self.num_samples_per_class*self.way_num
+        this_task_filenames = all_filenames[idx*one_episode_sample_num:(idx+1)*one_episode_sample_num]
+        epitr_sample_num = self.shot_num
+        epite_sample_num = self.episode_test_sample_num
+
+        this_task_tr_filenames = []
+        this_task_tr_labels = []
+        this_task_te_filenames = []
+        this_task_te_labels = []
+        for class_k in range(self.way_num):
+            this_class_filenames = this_task_filenames[class_k*self.num_samples_per_class:(class_k+1)*self.num_samples_per_class]
+            this_class_label = labels[class_k*self.num_samples_per_class:(class_k+1)*self.num_samples_per_class]
+            this_task_tr_filenames += this_class_filenames[0:epitr_sample_num]
+            this_task_tr_labels += this_class_label[0:epitr_sample_num]
+            this_task_te_filenames += this_class_filenames[epitr_sample_num:]
+            this_task_te_labels += this_class_label[epitr_sample_num:]
+
+        this_inputa, this_labela = self.process_batch(this_task_tr_filenames, this_task_tr_labels, epitr_sample_num, reshape_with_one=False, dont_shuffle_batch = dont_shuffle_batch)
+        this_inputb, this_labelb = self.process_batch(this_task_te_filenames, this_task_te_labels, epite_sample_num, reshape_with_one=False, dont_shuffle_batch = dont_shuffle_batch)
+
+        return this_inputa, this_labela, this_inputb, this_labelb
+
 
 cathegories = 5
 shots = args.shots

mini_imagenet_dataloader.py

@@ -141,13 +141,12 @@ def load_list(self, phase='train'):
         else:
             print('Please select vaild phase')
 
-    def process_batch(self, input_filename_list, input_label_list, batch_sample_num, reshape_with_one=True, dont_shuffle_batch = False):
+    def process_batch(self, input_filename_list, input_label_list, batch_sample_num, reshape_with_one=True):
         new_path_list = []
         new_label_list = []
         for k in range(batch_sample_num):
             class_idxs = list(range(0, self.way_num))
-            if not dont_shuffle_batch:
-                random.shuffle(class_idxs)
+            random.shuffle(class_idxs)
             for class_idx in class_idxs:
                 true_idx = class_idx*batch_sample_num + k
                 new_path_list.append(input_filename_list[true_idx])
@@ -175,7 +174,7 @@ def one_hot(self, inp):
             out[idx, inp[idx]] = 1
         return out
 
-    def get_batch(self, phase='train', idx=0, dont_shuffle_batch = False):
+    def get_batch(self, phase='train', idx=0):
         if phase=='train':
             all_filenames = self.train_filenames
             labels = self.train_labels 
@@ -205,7 +204,7 @@ def get_batch(self, phase='train', idx=0, dont_shuffle_batch = False):
             this_task_te_filenames += this_class_filenames[epitr_sample_num:]
             this_task_te_labels += this_class_label[epitr_sample_num:]
 
-        this_inputa, this_labela = self.process_batch(this_task_tr_filenames, this_task_tr_labels, epitr_sample_num, reshape_with_one=False, dont_shuffle_batch = dont_shuffle_batch)
-        this_inputb, this_labelb = self.process_batch(this_task_te_filenames, this_task_te_labels, epite_sample_num, reshape_with_one=False, dont_shuffle_batch = dont_shuffle_batch)
+        this_inputa, this_labela = self.process_batch(this_task_tr_filenames, this_task_tr_labels, epitr_sample_num, reshape_with_one=False)
+        this_inputb, this_labelb = self.process_batch(this_task_te_filenames, this_task_te_labels, epite_sample_num, reshape_with_one=False)
 
         return this_inputa, this_labela, this_inputb, this_labelb