fix test script

example_alexnet.py

@@ -78,7 +78,7 @@ def get_model(inputs, is_training):
 def get_config():
     basename = os.path.basename(__file__)
     log_dir = os.path.join('train_log', basename[:basename.rfind('.')])
-    logger.set_logger_dir(log_dir)
+    logger.set_logger_file(os.path.join(log_dir, 'training.log'))
 
     dataset_train = FakeData([(227,227,3), tuple()], 10)
     dataset_train = BatchData(dataset_train, 10)
@@ -158,7 +158,7 @@ if __name__ == '__main__':
     if args.gpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
 
-    #start_train(get_config())
+    start_train(get_config())
 
     # run alexnet with given model (in npy format)
     run_test('alexnet-tuned.npy')

scripts/dump_model_params.py

@@ -11,8 +11,6 @@ import imp
 from tensorpack.utils import *
 from tensorpack.utils import sessinit
 from tensorpack.dataflow import *
-from tensorpack.predict import DatasetPredictor
-
 
 parser = argparse.ArgumentParser()
 parser.add_argument(dest='config')
@@ -30,6 +28,3 @@ with tf.Graph().as_default() as G:
     init.init(sess)
     with sess.as_default():
         sessinit.dump_session_params(args.output)
-
-
-

scripts/imgclassify.py

@@ -39,7 +39,7 @@ with tf.Graph().as_default() as G:
     ds = ImageFromFile(args.images, 3, resize=(227, 227))
     predictor = DatasetPredictor(config, ds, batch=128)
     res = predictor.get_all_result()
-    res = [k[1] for k in res]
+    res = [k.output for k in res]
 
     if args.output_type == 'label':
         for r in res:

tensorpack/predict.py

@@ -4,7 +4,7 @@
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 import tensorflow as tf
-from itertools import count
+from itertools import count, izip
 import argparse
 from collections import namedtuple
 import numpy as np
@@ -93,7 +93,7 @@ def get_predict_func(config):
         assert len(input_map) == len(dp), \
             "Graph has {} inputs but dataset only gives {} components!".format(
                     len(input_map), len(dp))
-        feed = dict(zip(input_map, dp))
+        feed = dict(izip(input_map, dp))
         if output_var_names is not None:
             results = sess.run(output_vars, feed_dict=feed)
             return results

tensorpack/utils/sessinit.py

@@ -56,6 +56,7 @@ class ParamRestore(SessionInit):
             sess.run(var.assign(value))
 
 def dump_session_params(path):
+    """ dump value of all trainable variables to a dict"""
     var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
     result = {}
     for v in var: