Adding panoptic evaluation tools and update internal changes. (#6320)

* Internal changes

PiperOrigin-RevId: 237183552

* update readme

PiperOrigin-RevId: 237184584

research/deeplab/export_model.py

@@ -129,10 +129,10 @@ def main(unused_argv):
           model_options=model_options,
           eval_scales=FLAGS.inference_scales,
           add_flipped_images=FLAGS.add_flipped_images)
-
-    predictions = tf.cast(predictions[common.OUTPUT_TYPE], tf.float32)
+    raw_predictions = tf.identity(
+        tf.cast(predictions[common.OUTPUT_TYPE], tf.float32),
+        _RAW_OUTPUT_NAME)
     # Crop the valid regions from the predictions.
-    raw_predictions = tf.identity(predictions, _RAW_OUTPUT_NAME)
     semantic_predictions = tf.slice(
         raw_predictions,
         [0, 0, 0],

research/deeplab/g3doc/installation.md

@@ -26,8 +26,9 @@ The remaining libraries can be installed on Ubuntu 14.04 using via apt-get:
 
 ```bash
 sudo apt-get install python-pil python-numpy
-sudo pip install jupyter
-sudo pip install matplotlib
+pip install --user jupyter
+pip install --user matplotlib
+pip install --user PrettyTable
 ```
 
 ## Add Libraries to PYTHONPATH
@@ -38,7 +39,13 @@ tensorflow/models/research/:
 
 ```bash
 # From tensorflow/models/research/
-export PYTHONPATH=$PYTHONPATH:`pwd`
+export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
+
+# [Optional] for panoptic evaluation, you might need panopticapi:
+# https://github.com/cocodataset/panopticapi
+# Please clone it to a local directory ${PANOPTICAPI_DIR}
+touch ${PANOPTICAPI_DIR}/panopticapi/__init__.py
+export PYTHONPATH=$PYTHONPATH:${PANOPTICAPI_DIR}/panopticapi
 ```
 
 Note: This command needs to run from every new terminal you start. If you wish

research/deeplab/train.py

@@ -485,6 +485,7 @@ def main(unused_argv):
             config=session_config,
             scaffold=scaffold,
             checkpoint_dir=FLAGS.train_logdir,
+            summary_dir=FLAGS.train_logdir,
             log_step_count_steps=FLAGS.log_steps,
             save_summaries_steps=FLAGS.save_summaries_secs,
             save_checkpoint_secs=FLAGS.save_interval_secs,

research/marco/Automated_Marco.py

-#!/usr/bin/python
-# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-import tensorflow as tf 
-import csv 
-import os 
-import argparse
-
-
-""" 
-usage: 
-Processes all .jpg, .png, .bmp and .gif files found in the specified directory and its subdirectories.
- --PATH ( Path to directory of images or path to directory with subdirectory of images). e.g Path/To/Directory/
- --Model_PATH path to the tensorflow model
-"""
-
-
-parser = argparse.ArgumentParser(description='Crystal Detection Program')
-
-
-parser.add_argument('--PATH', type=str, help='path to image directory. Recursively finds all image files in directory and  sub directories') # path to image directory or containing sub directories. 
-parser.add_argument('--MODEL_PATH', type=str, default='./savedmodel',help='the file path to the tensorflow model ') 
-args = vars(parser.parse_args())
-PATH = args['PATH']
-model_path = args['MODEL_PATH']
-
-
-crystal_images = [os.path.join(dp, f) for dp, dn, filenames in os.walk(PATH) for f in filenames if os.path.splitext(f)[1] in ['.jpg','png','bmp','gif']]
-size = len(crystal_images)
-
-def load_images(file_list):
-    for i in file_list:
-        files = open(i,'rb')
-        yield {"image_bytes":[files.read()]},i
-
-
-
-iterator =  load_images(crystal_images)
-
-with open(PATH +'results.csv', 'w') as csvfile:
-    Writer = csv.writer(csvfile, delimiter=' ',quotechar=' ', quoting=csv.QUOTE_MINIMAL)
-
-    predicter= tf.contrib.predictor.from_saved_model(model_path)
-    dic = {}
-
-
-    k = 0
-    for _ in range(size):
-            
-                data,name = next(iterator)
-                results = predicter(data)
-           
-                vals =results['scores'][0]
-                classes = results['classes'][0]
-                dictionary = dict(zip(classes,vals))
-    
-                print('Image path: '+ name+' Crystal: '+str(dictionary[b'Crystals'])+' Other: '+ str(dictionary[b'Other'])+' Precipitate: '+ str(dictionary[b'Precipitate'])+' Clear: '+ str(dictionary[b'Clear']))
-                Writer.writerow(['Image path: '+ name,'Crystal: '+str(dictionary[b'Crystals']),'Other: '+ str(dictionary[b'Other']),'Precipitate: '+ str(dictionary[b'Precipitate']),'Clear: '+ str(dictionary[b'Clear'])])
-  
+#!/usr/bin/python
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import tensorflow as tf 
+import csv 
+import os 
+import argparse
+
+
+""" 
+usage: 
+Processes all .jpg, .png, .bmp and .gif files found in the specified directory and its subdirectories.
+ --PATH ( Path to directory of images or path to directory with subdirectory of images). e.g Path/To/Directory/
+ --Model_PATH path to the tensorflow model
+"""
+
+
+parser = argparse.ArgumentParser(description='Crystal Detection Program')
+
+
+parser.add_argument('--PATH', type=str, help='path to image directory. Recursively finds all image files in directory and  sub directories') # path to image directory or containing sub directories. 
+parser.add_argument('--MODEL_PATH', type=str, default='./savedmodel',help='the file path to the tensorflow model ') 
+args = vars(parser.parse_args())
+PATH = args['PATH']
+model_path = args['MODEL_PATH']
+
+
+crystal_images = [os.path.join(dp, f) for dp, dn, filenames in os.walk(PATH) for f in filenames if os.path.splitext(f)[1] in ['.jpg','png','bmp','gif']]
+size = len(crystal_images)
+
+def load_images(file_list):
+    for i in file_list:
+        files = open(i,'rb')
+        yield {"image_bytes":[files.read()]},i
+
+
+
+iterator =  load_images(crystal_images)
+
+with open(PATH +'results.csv', 'w') as csvfile:
+    Writer = csv.writer(csvfile, delimiter=' ',quotechar=' ', quoting=csv.QUOTE_MINIMAL)
+
+    predicter= tf.contrib.predictor.from_saved_model(model_path)
+    dic = {}
+
+
+    k = 0
+    for _ in range(size):
+            
+                data,name = next(iterator)
+                results = predicter(data)
+           
+                vals =results['scores'][0]
+                classes = results['classes'][0]
+                dictionary = dict(zip(classes,vals))
+    
+                print('Image path: '+ name+' Crystal: '+str(dictionary[b'Crystals'])+' Other: '+ str(dictionary[b'Other'])+' Precipitate: '+ str(dictionary[b'Precipitate'])+' Clear: '+ str(dictionary[b'Clear']))
+                Writer.writerow(['Image path: '+ name,'Crystal: '+str(dictionary[b'Crystals']),'Other: '+ str(dictionary[b'Other']),'Precipitate: '+ str(dictionary[b'Precipitate']),'Clear: '+ str(dictionary[b'Clear'])])
+