Commit 5bc70b70 authored by Oleg Dzhimiev's avatar Oleg Dzhimiev
Browse files

cleanup

parent 5414fe8c

infer_qcds_01.py

+13 −6
Original line number Original line Diff line number Diff line
@@ -267,10 +267,16 @@ with tf.Session() as sess: 
    

    

    saver.restore(sess, files["checkpoints"])

    saver.restore(sess, files["checkpoints"])

    

    

    #tf.add_to_collection(GraphKeys.GLOBAL_VARIABLES,rv_stage1_out)

    '''

        rv_stage1_out belongs to GraphKeys.LOCAL_VARIABLES

        Now when weights/biases are restored from 'checkpoints', 

        that do not have this variable, add it to globals.

        Actually it could have been declared right here - this

        needs testing.

    '''

    tf.add_to_collection(GraphKeys.GLOBAL_VARIABLES, rv_stage1_out)

    

    

    saver.save(sess, files["inference"])  #TODO: move to different subdir

    saver.save(sess, files["inference"])

    #saver2.save(sess, files["inference"]+"_2")  #TODO: move to different subdir

      

      

    merged = tf.summary.merge_all()

    merged = tf.summary.merge_all()

    writer = tf.summary.FileWriter(ROOT_PATH, sess.graph)

    writer = tf.summary.FileWriter(ROOT_PATH, sess.graph)
@@ -330,7 +336,8 @@ with tf.Session() as sess: 
        """

        """

        image_data[nimg] = None

        image_data[nimg] = None

    

    

    meta_graph_def = tf.train.export_meta_graph(files["inference"]+'.meta')

    # is this needed? why would it be?

    #meta_graph_def = tf.train.export_meta_graph(files["inference"]+'.meta')

                

                

    

    

    if lf:

    if lf:

infer_qcds_02.py

+7 −5
Original line number Original line Diff line number Diff line
@@ -265,12 +265,14 @@ with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer())

    sess.run(tf.global_variables_initializer())

    sess.run(tf.local_variables_initializer())

    sess.run(tf.local_variables_initializer())

    

    

    saver.restore(sess, files["checkpoints"])

    # so, here I need to restore from inference and save to inference_global

    #saver.restore(sess, files["checkpoints"])

    saver.restore(sess, files["inference"])

    

    

    #tf.add_to_collection(GraphKeys.GLOBAL_VARIABLES,rv_stage1_out)

    # now add to global

    tf.add_to_collection(GraphKeys.GLOBAL_VARIABLES,rv_stage1_out)

    

    

    saver.save(sess, files["inference"])  #TODO: move to different subdir

    saver.save(sess, 'data_sets/tf_data_5x5_main_13_heur/inference_global/model')

    #saver2.save(sess, files["inference"]+"_2")  #TODO: move to different subdir





    merged = tf.summary.merge_all()

    merged = tf.summary.merge_all()

    writer = tf.summary.FileWriter(ROOT_PATH, sess.graph)

    writer = tf.summary.FileWriter(ROOT_PATH, sess.graph)

infer_qcds_graph_01.py

+28 −37
Original line number Original line Diff line number Diff line
@@ -129,31 +129,29 @@ try: 
except:

except:

    pass

    pass





from tensorflow.python.framework.ops import GraphKeys



with tf.Session()  as sess:

with tf.Session()  as sess:

    

    

    # default option

    # Actually, refresh all the time and have an extra script to restore from it.  

    use_saved_model = False

    # use_Saved_Model = False

    if os.path.isdir(dirs['exportdir']):

    

        # check if dir contains "Saved Model" model

    #if os.path.isdir(dirs['exportdir']):

        use_saved_model = tf.saved_model.loader.maybe_saved_model_directory(dirs['exportdir'])

    #    # check if dir contains "Saved Model" model

    #    use_saved_model = tf.saved_model.loader.maybe_saved_model_directory(dirs['exportdir'])



    #if use_saved_model:

    #    print("Model restore: using Saved_Model model MetaGraph protocol buffer")

    #    meta_graph_source = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], dirs['exportdir'])

    #else:

    

    

    if use_saved_model:

        print("Model restore: using Saved_Model model MetaGraph protocol buffer")

        meta_graph_source = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], dirs['exportdir'])

    else:

        print("Model restore: using conventionally saved model, but saving Saved Model for the next run")

    meta_graph_source = files["inference"]+'.meta'

    meta_graph_source = files["inference"]+'.meta'

    

    print("Model restore: using conventionally saved model, but saving Saved Model for the next run")

    print("MetaGraph source = "+str(meta_graph_source))

    print("MetaGraph source = "+str(meta_graph_source))

        #meta_graph_source = files["inference"]+'_2.meta'

        # remove 'exportdir' even it exsits and has anything

        shutil.rmtree(dirs['exportdir'], ignore_errors=True)

        builder = tf.saved_model.builder.SavedModelBuilder(dirs['exportdir'])





    infer_saver = tf.train.import_meta_graph(meta_graph_source)

    infer_saver = tf.train.import_meta_graph(meta_graph_source)

    

    

    graph=tf.get_default_graph()

    graph=tf.get_default_graph()

    

    ph_corr2d =           graph.get_tensor_by_name('ph_corr2d:0') 

    ph_corr2d =           graph.get_tensor_by_name('ph_corr2d:0') 

    ph_target_disparity = graph.get_tensor_by_name('ph_target_disparity:0')

    ph_target_disparity = graph.get_tensor_by_name('ph_target_disparity:0')

    ph_ntile =            graph.get_tensor_by_name('ph_ntile:0')

    ph_ntile =            graph.get_tensor_by_name('ph_ntile:0')
@@ -164,27 +162,17 @@ with tf.Session() as sess: 
    if not USE_SPARSE_ONLY: #Does it reduce the graph size?

    if not USE_SPARSE_ONLY: #Does it reduce the graph size?

        stage2_out_full = graph.get_tensor_by_name('Disparity_net/stage2_out_full:0') 

        stage2_out_full = graph.get_tensor_by_name('Disparity_net/stage2_out_full:0') 





    '''

    if not use_saved_model:

        rv_stage1_out = tf.get_variable("rv_stage1_out",

                                        shape=[78408, 32],

                                        dtype=tf.float32,

                                        initializer=tf.zeros_initializer)

                                        #collections = [GraphKeys.LOCAL_VARIABLES],trainable=False)

    '''





    sess.run(tf.global_variables_initializer())

    sess.run(tf.global_variables_initializer())

    sess.run(tf.local_variables_initializer())

    sess.run(tf.local_variables_initializer())





    if not use_saved_model:

        infer_saver.restore(sess, files["inference"]) # after initializers, of course

    else:

        infer_saver.restore(sess, dirs['exportdir']+"/variables/variables.data-00000-of-00001")





    #infer_saver.restore(sess, files["inference"]+"_2") # after initializers, of course

    infer_saver.restore(sess, files["inference"])

    

    

    

    merged = tf.summary.merge_all()

    merged = tf.summary.merge_all()

    writer = tf.summary.FileWriter(ROOT_PATH, sess.graph)

    writer = tf.summary.FileWriter(ROOT_PATH, sess.graph)

    

    lf = None

    lf = None

    if LOGPATH:

    if LOGPATH:

        lf=open(LOGPATH,"w") #overwrite previous (or make it "a"?

        lf=open(LOGPATH,"w") #overwrite previous (or make it "a"?
@@ -234,11 +222,14 @@ with tf.Session() as sess: 
        """

        """

        image_data[nimg] = None

        image_data[nimg] = None

    

    

    if not use_saved_model:

    #builder.add_meta_graph_and_variables(sess,PB_TAGS)

    #builder.add_meta_graph_and_variables(sess,PB_TAGS)

    # clean

    shutil.rmtree(dirs['exportdir'], ignore_errors=True)

    # save MetaGraph to Saved_Model as *.pb

    builder = tf.saved_model.builder.SavedModelBuilder(dirs['exportdir'])

    builder.add_meta_graph_and_variables(sess,[tf.saved_model.tag_constants.SERVING])

    builder.add_meta_graph_and_variables(sess,[tf.saved_model.tag_constants.SERVING])

        builder.save(True)

    #builder.save(True)

        #builder.save(False)

    builder.save(False) # True = *.pbtxt, False = *.pb

    

    

    if lf:

    if lf:

        lf.close()

        lf.close()