minor changes

Author: tnikolla

grasp_img_proc.py

@@ -146,7 +146,7 @@ def distorted_inputs(data_files, num_epochs, train=True, batch_size=None):
     return images, bboxes
 
 
-def inputs(data_files, batch_size, num_epochs, train=False):
+def inputs(data_files, num_epochs, train=False, batch_size=None):
     with tf.device('/cpu:0'):
         images, bboxes = batch_inputs(
             data_files, train, num_epochs, batch_size,

image_processing.py

@@ -78,7 +78,8 @@ def batch_inputs(data_files, train, num_epochs, batch_size,
                                                         shuffle=True,
                                                         capacity=16)
     else:
-        filename_queue = tf.train.string_input_producer(ata_files,
+        filename_queue = tf.train.string_input_producer(data_files,
+                                                        num_epochs,
                                                         shuffle=False,
                                                         capacity=1)
 
@@ -136,7 +137,7 @@ def distorted_inputs(data_files, num_epochs, train=True, batch_size=None):
             num_readers=FLAGS.num_readers)
     return images, labels
 
-def inputs(data_files, batch_size, train=False, num_epochs=None):
+def inputs(data_files, num_epochs=None, train=False, batch_size=None):
     with tf.device('/cpu:0'):
         images, labels = batch_inputs(
             data_files, train, num_epochs, batch_size,

imagenet_classifier.py

@@ -13,18 +13,18 @@
 TRAIN_FILE = '/root/imagenet-data/train-00001-of-01024'
 VALIDATION_FILE = '/root/imagenet-data/validation-00004-of-00128'
 
-def data_files():
-    tf_record_pattern = os.path.join(FLAGS.data_dir, '%s-*' % FLAGS.train)
+def data_files(dataset):
+    tf_record_pattern = os.path.join(FLAGS.data_dir, '%s-*' % dataset)
     data_files = tf.gfile.Glob(tf_record_pattern)
     return data_files
 
 def run_training():
-    #tf.reset_default_graph()
-    data_files_ = TRAIN_FILE
+    #data_files_ = TRAIN_FILE
     #data_files_ = VALIDATION_FILE
-    #data_files_ = data_files()
+    data_files_ = data_files(FLAGS.train_or_validation)
     images, labels = image_processing.distorted_inputs(
-        [data_files_], FLAGS.num_epochs, batch_size=FLAGS.batch_size)
+           data_files_, FLAGS.num_epochs, batch_size=FLAGS.batch_size)
+    
     labels = tf.one_hot(labels, 1000)   
     logits = inference(images)
     loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
@@ -57,12 +57,9 @@ def run_training():
             _, loss_value, pred, acc = sess.run(
                 [train_op, loss, correct_pred, accuracy])
             duration = time.time() - start_batch
-            if step % 10 == 0:             
+            if step % 100 == 0:             
                 print('Step %d | loss = %.2f | accuracy = %.2f (%.3f sec/batch)')%(
                 step, loss_value, acc, duration)
-            if step % 500 == 0:
-                summary = sess.run(merged_summary_op)
-                summary_writer.add_summary(summary, step*FLAGS.batch_size)
             if step % 5000 == 0:
                 saver.save(sess, FLAGS.model_path)
 
@@ -75,8 +72,58 @@ def run_training():
     coord.join(threads)
     sess.close()
 
+def evaluation():
+    #data_files_ = TRAIN_FILE
+    data_files_ = data_files(FLAGS.train_or_validation)
+    images, labels = image_processing.inputs(
+            data_files_, FLAGS.num_epochs, batch_size=FLAGS.batch_size)
+    
+    labels = tf.one_hot(labels, 1000)   
+    logits = inference(images)
+    correct_pred = tf.equal(tf.arg_max(logits,1), tf.argmax(labels,1))
+    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
+    
+    init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
+    sess = tf.Session()
+    sess.run(init_op)
+    coord = tf.train.Coordinator()
+    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
+    
+    #save/restore model
+    d={}
+    l = ['w1', 'b1', 'w2', 'b2', 'w3', 'b3', 'w4', 'b4', 'w5', 'b5', 'w_fc1', 'b_fc1', 'w_fc2', 'b_fc2', 'w_output', 'b_output']
+    for i in l:
+        d[i] = [v for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) if v.name == i+':0'][0]
+    saver = tf.train.Saver(d)
+    saver.restore(sess, FLAGS.model_path)
+    
+    try:
+        step = 0
+        start_time = time.time()
+        while not coord.should_stop():
+            start_batch = time.time()
+            acc = sess.run(accuracy)
+            duration = time.time() - start_batch
+            print('Step %d | accuracy = %.2f (%.3f sec/batch)')%(
+                    step, acc, duration)
+            step +=1
+    except tf.errors.OutOfRangeError:
+        print('Done evaluating for %d epochs, %d steps, %.1f min.' % (FLAGS.num_epochs, step, (time.time()-start_time)/60))
+    finally:
+        coord.request_stop()
+
+    coord.join(threads)
+    sess.close()
+
 def main(_):
-    run_training()
+    if FLAGS.train_or_validation == 'train':
+        print '   ***   run training.'
+        print FLAGS.train_or_validation
+        run_training()
+    else:
+        print '   ***   run validation.'
+        print FLAGS.train_or_validation
+        evaluation()
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
@@ -117,7 +164,7 @@ def main(_):
         help='Variables for the model.'
     )
     parser.add_argument(
-        '--train',
+        '--train_or_validation',
         type=str,
         default='train',
         help='Train or evaluate the dataset'