DOC: add copyright header + a bit of refactoring

python/create_model_ienco-m3_patchbased.py

+# -*- coding: utf-8 -*-
+#==========================================================================
+#
+#   Copyright Remi Cresson, Dino Ienco (IRSTEA)
+#
+#   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.txt
+#
+#   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 sys
 import os
 import numpy as np
@@ -14,35 +32,6 @@ from sklearn.utils import shuffle
 from sklearn.metrics import confusion_matrix
 from tricks import *
   
-def export_model(sess, 
-                 export_dir,
-                 x_cnn_placeholder,
-                 x_rnn_placeholder,
-                 is_training_placeholder,
-                 testPrediction):
-  """ export a SavedModel
-  """
-  
-  # Update the export dir
-  model_dir = export_dir + "/saved_model/"
-  if os.path.exists(model_dir):
-    shutil.rmtree(model_dir)
-    
-  print("Export model in " + model_dir)
-      
-  # Add a builder (for LoadSavedModel)
-  builder = tf.saved_model.builder.SavedModelBuilder(model_dir)
-  signature_def_map= {
-    "model": tf.saved_model.signature_def_utils.predict_signature_def(
-    inputs =  {"x_cnn"     : x_cnn_placeholder,
-               "x_rnn"     : x_rnn_placeholder,
-               "is_training" : is_training_placeholder},
-    outputs = {"prediction"  : testPrediction})
-  }
-  builder.add_meta_graph_and_variables(sess,[tf.saved_model.tag_constants.TRAINING],signature_def_map)
-  builder.add_meta_graph([tf.saved_model.tag_constants.SERVING])
-  builder.save()
-  
 def checkTest(ts_data, vhsr_data, batchsz, label_test):
   tot_pred = []
 #  gt_test = []
@@ -61,10 +50,6 @@ def checkTest(ts_data, vhsr_data, batchsz, label_test):
                              dropout:0.0,
                              x_cnn:batch_cnn_x})
 
-    del batch_rnn_x
-    del batch_cnn_x
-    del batch_y
-    
     for el in pred_temp:
       tot_pred.append( el )
 
@@ -241,8 +226,8 @@ n_channels   = 4
 nclasses     = 8
 
 # check number of arguments
-if len(sys.argv) != 7:
-  print("Usage : <ts_train> <vhs_train> <label_train> <ts_valid> <vhs_valid> <label_valid>")
+if len(sys.argv) != 8:
+  print("Usage : <ts_train> <vhs_train> <label_train> <ts_valid> <vhs_valid> <label_valid> <export_dir>")
   sys.exit(1)
 
 ts_train    = read_samples(sys.argv[1])
@@ -257,6 +242,8 @@ label_test  = read_samples(sys.argv[6])
 label_test = np.int32(label_test)
 print_histo(label_test, "label_test")
 
+export_dir = read_samples(sys.argv[7])
+
 x_rnn = tf.placeholder(tf.float32,[None, 1, 1, n_dims*n_timestamps],name="x_rnn")
 x_cnn = tf.placeholder(tf.float32,[None, patch_window, patch_window, n_channels],name="x_cnn")
 y     = tf.placeholder(tf.int32,[None, 1, 1, 1],name="y")
@@ -324,19 +311,12 @@ for e in range(hm_epochs):
     lossi+=loss
     accS+=acc
     
-    del batch_rnn_x
-    del batch_cnn_x
-    del batch_y
-    
-    
   print "Epoch:",e,"Train loss:",lossi/iterations,"| accuracy:",accS/iterations
   
   c_loss = lossi/iterations
   
   if c_loss < best_loss:
-    save_path = saver.save(sess, "models/model")
-    print("Model saved in path: %s" % save_path)
     best_loss = c_loss
-    export_model(sess, "/tmp/m3_export", x_cnn, x_rnn, is_training_ph, testPrediction)
+    CreateSavedModel(sess, ["x_cnn:0","x_rnn:0","is_training:0"], ["prediction:0"], export_dir)
 
   test_acc = checkTest(ts_test, vhsr_test, 1024, label_test)
\ No newline at end of file

python/create_model_maggiori17_fullyconv.py

+# -*- coding: utf-8 -*-
+#==========================================================================
+#
+#   Copyright Remi Cresson (IRSTEA)
+#
+#   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.txt
+#
+#   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.
+#
+#==========================================================================*/
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -89,12 +107,11 @@ def main(unused_argv):
 
   # check number of arguments
   if len(sys.argv) != 4:
-    print("Usage : <patches> <labels> <output_model_dir>")
+    print("Usage : <patches> <labels> <export_dir>")
     sys.exit(1)
 
   # Export dir
-  log_dir = sys.argv[3] + '/model_checkpoints/'
-  export_dir = sys.argv[3] + '/model_export/'
+  export_dir = sys.argv[3]
 
   print("loading dataset")
 
@@ -272,13 +289,9 @@ def main(unused_argv):
         if step % 10 == 0:
           # Print status to stdout.
           print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
-          #print('Step %d: (%.3f sec)' % (step, duration))
-
 
       # Save a checkpoint and evaluate the model periodically.
       if (curr_epoch + 1) % 1 == 0:
-        checkpoint_file = os.path.join(log_dir, 'model.ckpt')
-        saver.save(sess, checkpoint_file, global_step=step)
         # Evaluate against the training set.
         print('Training Data Eval:')
         do_eval2(sess,
@@ -301,16 +314,7 @@ def main(unused_argv):
                 batch_size)
                 
         # Let's export a SavedModel
-        shutil.rmtree(export_dir)
-        builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
-        signature_def_map= {
-        "model": tf.saved_model.signature_def_utils.predict_signature_def(
-            inputs= {"x1": xs_placeholder},
-            outputs= {"prediction": testPrediction})
-        }
-        builder.add_meta_graph_and_variables(sess, [tf.saved_model.tag_constants.TRAINING], signature_def_map)
-        builder.add_meta_graph([tf.saved_model.tag_constants.SERVING])
-        builder.save()
+        CreateSavedModel(sess, ["x1:0"], ["prediction:0"], export_dir)
 
   quit()