Allow model_inspect_test to run under different tf versions.

Also fixed a few simple issues.

Author: mingxingtan

efficientdet/dataset/create_coco_tfrecord.py

@@ -339,7 +339,7 @@ def _get_caption_annotation(image_id):
                total_num_annotations_skipped)
 
 
-def main():
+def main(_):
   assert FLAGS.image_dir, '`image_dir` missing.'
   assert (FLAGS.image_info_file or FLAGS.object_annotations_file or
           FLAGS.caption_annotations_file), ('All annotation files are '

efficientdet/dataset/create_pascal_tfrecord.py

@@ -207,7 +207,7 @@ def dict_to_tf_example(data,
   return example
 
 
-def main():
+def main(_):
   if FLAGS.set not in SETS:
     raise ValueError('set must be in : {}'.format(SETS))
   if FLAGS.year not in YEARS:

efficientdet/g3doc/faq.md

@@ -26,5 +26,5 @@ We have some internal fixes, which could potentially be available with the next
 ### How can I run all tests?
 
     !export PYTHONPATH="`pwd`:$PYTHONPATH"
-    !find . -name "*_test.py" | parallel python &> test.log \
-      && echo "All passed" || echo "Failed! Search keyword FAILED in test.log"
+    !find . -name "*_test.py" | parallel python &> /tmp/test.log \
+      && echo "All passed" || echo "Failed! Search keyword FAILED in /tmp/test.log"

efficientdet/inference.py

@@ -725,7 +725,6 @@ def export(self, output_dir, frozen_pb=True, tflite_path=None):
       logging.info('Free graph saved at %s', pb_path)
 
     if tflite_path:
-      print(tf.__version__)
       input_name = signitures['image_arrays'].op.name
       height, width = self.params['image_size']
       input_shapes = {input_name: [None, height, width, 3]}

efficientdet/main.py

@@ -120,7 +120,7 @@
 FLAGS = flags.FLAGS
 
 
-def main():
+def main(_):
   if FLAGS.use_tpu:
     tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
         FLAGS.tpu,

efficientdet/model_inspect.py

@@ -370,7 +370,7 @@ def benchmark_model(self, warmup_runs, bm_runs, num_threads,
       for i in range(warmup_runs):
         start_time = time.time()
         sess.run(output_name, feed_dict={input_name: img})
-        print('Warm up: {} {:.4f}s'.format(i, time.time() - start_time))
+        logging.info('Warm up: {} {:.4f}s'.format(i, time.time() - start_time))
 
       print('Start benchmark runs total={}'.format(bm_runs))
       start = time.perf_counter()
@@ -448,7 +448,7 @@ def run_model(self, runmode, **kwargs):
       raise ValueError('Unkown runmode {}'.format(runmode))
 
 
-def main():
+def main(_):
   if tf.io.gfile.exists(FLAGS.logdir) and FLAGS.delete_logdir:
     logging.info('Deleting log dir ...')
     tf.io.gfile.rmtree(FLAGS.logdir)

efficientdet/model_inspect_test.py

@@ -38,7 +38,9 @@ class ModelInspectTest(tf.test.TestCase):
 
   def setUp(self):
     super(ModelInspectTest, self).setUp()
-    self.tempdir = tempfile.gettempdir()
+    sys_tempdir = tempfile.gettempdir()
+    self.tempdir = os.path.join(sys_tempdir, '_inspect_test')
+    os.mkdir(self.tempdir)
 
     np.random.seed(111)
     tf.random.set_random_seed(111)
@@ -59,6 +61,10 @@ def setUp(self):
         batch_size=1,
         hparams='')
 
+  def tearDown(self):
+    super(ModelInspectTest, self).tearDown()
+    shutil.rmtree(self.tempdir)
+
   def test_dry_run(self):
     inspector = model_inspect.ModelInspector(**self.params)
     inspector.run_model('dry')
@@ -88,7 +94,7 @@ def test_infer(self):
     self.assertTrue(os.path.exists(os.path.join(outdir, '0.jpg')))
 
     out = np.sum(np.array(Image.open(os.path.join(outdir, '0.jpg'))))
-    self.assertEqual(out, 168044958)
+    self.assertEqual(out // 1000000, 168)
 
   def test_saved_model(self):
     inspector = model_inspect.ModelInspector(**self.params)
@@ -126,7 +132,7 @@ def test_saved_model_infer(self):
     self.assertTrue(os.path.exists(os.path.join(outdir, '0.jpg')))
 
     out = np.sum(np.array(Image.open(os.path.join(outdir, '0.jpg'))))
-    self.assertEqual(out, 168044958)
+    self.assertEqual(out // 1000000, 168)
 
   def test_saved_model_infer_dynamic_batch(self):
     # Build saved model with dynamic batch size.
@@ -180,7 +186,7 @@ def test_saved_model_graph_infer(self):
     self.assertTrue(os.path.exists(os.path.join(outdir, '0.jpg')))
 
     out = np.sum(np.array(Image.open(os.path.join(outdir, '0.jpg'))))
-    self.assertEqual(out, 168044958)
+    self.assertEqual(out // 1000000, 168)
 
 
 if __name__ == '__main__':

efficientdet/visualize/vis_utils.py

@@ -956,7 +956,7 @@ def cdf_plot(values):
     ax.set_xlabel('fraction of examples')
     fig.canvas.draw()
     width, height = fig.get_size_inches() * fig.get_dpi()
-    image = np.fromstring(fig.canvas.tostring_rgb(), dtype='uint8').reshape(
+    image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8').reshape(
         1, int(height), int(width), 3)
     return image
   cdf_plot = tf.py_func(cdf_plot, [values], tf.uint8)
@@ -984,7 +984,7 @@ def hist_plot(values, bins):
     ax.set_xlabel('value')
     fig.canvas.draw()
     width, height = fig.get_size_inches() * fig.get_dpi()
-    image = np.fromstring(
+    image = np.frombuffer(
         fig.canvas.tostring_rgb(), dtype='uint8').reshape(
             1, int(height), int(width), 3)
     return image

efficientdet/visualize/vis_utils_test.py

@@ -168,7 +168,7 @@ def test_draw_bounding_boxes_on_image_tensors(self):
               min_score_thresh=0.2,
               keypoint_edges=keypoint_edges))
 
-      with self.test_session() as sess:
+      with self.session() as sess:
         sess.run(tf.global_variables_initializer())
 
         # Write output images for visualization.
@@ -215,7 +215,7 @@ def test_draw_bounding_boxes_on_image_tensors_with_track_ids(self):
               track_ids=track_ids,
               min_score_thresh=0.2))
 
-      with self.test_session() as sess:
+      with self.session() as sess:
         sess.run(tf.global_variables_initializer())
 
         # Write output images for visualization.
@@ -251,7 +251,7 @@ def test_draw_bounding_boxes_on_image_tensors_with_additional_channels(self):
               category_index,
               min_score_thresh=0.2))
 
-      with self.test_session() as sess:
+      with self.session() as sess:
         sess.run(tf.global_variables_initializer())
 
         final_images_np = sess.run(images_with_boxes)
@@ -280,7 +280,7 @@ def test_draw_bounding_boxes_on_image_tensors_grayscale(self):
               true_image_shape=image_shape,
               min_score_thresh=0.2))
 
-      with self.test_session() as sess:
+      with self.session() as sess:
         sess.run(tf.global_variables_initializer())
 
         final_images_np = sess.run(images_with_boxes)
@@ -336,15 +336,15 @@ def test_add_cdf_image_summary(self):
     values = [0.1, 0.2, 0.3, 0.4, 0.42, 0.44, 0.46, 0.48, 0.50]
     vis_utils.add_cdf_image_summary(values, 'PositiveAnchorLoss')
     cdf_image_summary = tf.get_collection(key=tf.GraphKeys.SUMMARIES)[0]
-    with self.test_session():
+    with self.session():
       cdf_image_summary.eval()
 
   def test_add_hist_image_summary(self):
     values = [0.1, 0.2, 0.3, 0.4, 0.42, 0.44, 0.46, 0.48, 0.50]
     bins = [0.01 * i for i in range(101)]
     vis_utils.add_hist_image_summary(values, bins, 'ScoresDistribution')
     hist_image_summary = tf.get_collection(key=tf.GraphKeys.SUMMARIES)[0]
-    with self.test_session():
+    with self.session():
       hist_image_summary.eval()
 
   def test_eval_metric_ops(self):
@@ -392,7 +392,7 @@ def test_eval_metric_ops(self):
     metric_ops = eval_metric_ops.get_estimator_eval_metric_ops(eval_dict)
     _, update_op = metric_ops[next(six.iterkeys(metric_ops))]
 
-    with self.test_session() as sess:
+    with self.session() as sess:
       sess.run(tf.global_variables_initializer())
       value_ops = {}
       for key, (value_op, _) in six.iteritems(metric_ops):