Working code for atr (color) classifier

color_classifiers/atr_data_io_helper.py

@@ -42,7 +42,7 @@ def atr_mini_batch_loader(json_filename, image_dir, mean_image, start_index, bat
     return (atr_images, atr_labels)
 
 def mean_image_batch(json_filename, image_dir, start_index, batch_size, img_height=100, img_width=100, channels=3):
-    batch = obj_mini_batch_loader(json_filename, image_dir, np.empty([]), start_index, batch_size, img_height, img_width, channels)
+    batch = atr_mini_batch_loader(json_filename, image_dir, np.empty([]), start_index, batch_size, img_height, img_width, channels)
     mean_image = np.mean(batch[0], 0)
     return mean_image
 

color_classifiers/eval_atr_classifier.py

@@ -5,33 +5,33 @@ import matplotlib.image as mpimg
 import numpy as np
 from scipy import misc
 import tensorflow as tf
-import obj_data_io_helper as shape_data_loader 
-from train_obj_classifier import placeholder_inputs, comp_graph_v_1, evaluation
+import atr_data_io_helper as atr_data_loader 
+from train_atr_classifier import placeholder_inputs, comp_graph_v_2, evaluation
 
 sess=tf.InteractiveSession()
 
 x, y, keep_prob = placeholder_inputs()
-y_pred = comp_graph_v_1(x, y, keep_prob)
+y_pred = comp_graph_v_2(x, y, keep_prob)
 
 accuracy = evaluation(y, y_pred)
 
 saver = tf.train.Saver()
 
-saver.restore(sess, '/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier_v_1/obj_classifier_9.ckpt')
+saver.restore(sess, '/home/tanmay/Code/GenVQA/Exp_Results/Atr_Classifier_v_1/obj_classifier_1.ckpt')
 
-mean_image = np.load('/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier_v_1/mean_image.npy')
+mean_image = np.load('/home/tanmay/Code/GenVQA/Exp_Results/Atr_Classifier_v_1/mean_image.npy')
 
 # Test Data
 test_json_filename = '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/test_anno.json'
 image_dir = '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/images'
 
 # Base dir for html visualizer
-html_dir = '/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier_v_1/html'
+html_dir = '/home/tanmay/Code/GenVQA/Exp_Results/Atr_Classifier_v_1/html'
 if not os.path.exists(html_dir):
     os.mkdir(html_dir)
 
 # HTML file writer
-html_writer = shape_data_loader.html_obj_table_writer(os.path.join(html_dir,'index.html'))
+html_writer = atr_data_loader.html_atr_table_writer(os.path.join(html_dir,'index.html'))
 col_dict={
     0: 'Grount Truth',
     1: 'Prediction',
@@ -47,7 +47,7 @@ shape_dict = {
 batch_size = 100
 correct = 0
 for i in range(50): 
-    test_batch = shape_data_loader.obj_mini_batch_loader(test_json_filename, image_dir, mean_image, 10000+i*batch_size, batch_size, 75, 75)
+    test_batch = atr_data_loader.atr_mini_batch_loader(test_json_filename, image_dir, mean_image, 10000+i*batch_size, batch_size, 75, 75)
     feed_dict_test={x: test_batch[0], y: test_batch[1], keep_prob: 1.0}
     result = sess.run([accuracy, y_pred], feed_dict=feed_dict_test)
     correct = correct + result[0]*batch_size

color_classifiers/train_atr_classifier.py

@@ -89,24 +89,23 @@ def comp_graph_v_1(x, y, keep_prob):
 
 def comp_graph_v_2(x, y, keep_prob):
     # Specify computation graph
-    W_conv1 = weight_variable([5, 5, 3, 10])
-    b_conv1 = bias_variable([10])
+    W_conv1 = weight_variable([5, 5, 3, 4])
+    b_conv1 = bias_variable([4])
     
     h_conv1 = tf.nn.relu(conv2d(x, W_conv1) + b_conv1)
     h_pool1 = max_pool_2x2(h_conv1)
     h_conv1_drop = tf.nn.dropout(h_pool1, keep_prob)
     
-    W_conv2 = weight_variable([5, 5, 10, 20])
-    b_conv2 = bias_variable([20])
+    W_conv2 = weight_variable([3, 3, 4, 8])
+    b_conv2 = bias_variable([8])
     
     h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
     h_pool2 = max_pool_2x2(h_conv2)
-    h_conv2_drop = tf.nn.dropout(h_pool2, keep_prob)
 
-    W_fc1 = weight_variable([7*7*20, 4])
+    W_fc1 = weight_variable([7*7*8, 4])
     b_fc1 = bias_variable([4])
     
-    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*20])
+    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*8])
     h_pool2_flat_drop = tf.nn.dropout(h_pool2_flat, keep_prob)
     
     y_pred = tf.nn.softmax(tf.matmul(h_pool2_flat_drop,W_fc1) + b_fc1)