prediction from frozen (.pb)

noveou_train_netvlad_v3.py

@@ -109,13 +109,17 @@ def on_epoch_start(self):
 
             self.D = self.pr.step_n_times(n_samples=self.n_samples_pitts, nP=self.nP, nN=self.nN, resize=self.resize, return_gray=self.return_gray, ENABLE_IMSHOW=self.ENABLE_IMSHOW )
             self.y = np.zeros( len(self.D) )
-            print 'len(D)=', len(self.D), '\tD[0].shape=', self.D[0].shape
+            print 'epoch=', self.epoch, '\tlen(D)=', len(self.D), '\tD[0].shape=', self.D[0].shape
 
 
-            # if self.epoch > 400:
-            if self.epoch > 400 and self.n_samples_pitts<0:
+            if self.epoch > 10:
+            # if self.epoch > 400 and self.n_samples_pitts<0:
                 # Data Augmentation after 400 epochs.
+                print tcolor.WARNING, 'do_typical_data_aug', tcolor.ENDC
                 self.D = do_typical_data_aug( self.D )
+            else:
+                print tcolor.WARNING, 'NO Data Augmentation', tcolor.ENDC
+
 
             print 'returned len(self.D)=', len(self.D), 'self.D[0].shape=', self.D[0].shape
             self.y = np.zeros( len(self.D) )
@@ -153,7 +157,7 @@ def signal_handler(sig, frame):
     #TODO: somehow get access to current epoch number and write the mdoel file accordingly
     print('You pressed Ctrl+C!')
     print 'Save Current Model : ',  int_logr.dir() + '/core_modelX.keras'
-    model.save( int_logr.dir() + '/core_modelX.keras' )
+    # model.save( int_logr.dir() + '/core_modelX.keras' )
     model.save( int_logr.dir() + '/modelarch_and_weights.X.h5' )
     sys.exit(0)
 
@@ -174,7 +178,7 @@ def signal_handler(sig, frame):
     #---
     image_nrows = 240
     image_ncols = 320
-    image_nchnl = 3 #cannot make this to 1 if u want to use VGG-pretained-weights (from imagenet), bcoz imagenet was trained with color images. However this is usable (can set chanls=1) if you want to train from scratch
+    image_nchnl = 1 #cannot make this to 1 if u want to use VGG-pretained-weights (from imagenet), bcoz imagenet was trained with color images. However this is usable (can set chanls=1) if you want to train from scratch
 
     #---
     #   note: some other thing thats need to be set
@@ -183,7 +187,7 @@ def signal_handler(sig, frame):
 
     CNN_type =  'mobilenetv2'       #'mobilenet', 'vgg16', 'mobilenetv2'
     layer_name= 'block_9_add' #'conv_pw_7_relu', 'block5_pool', 'block_9_add'
-    init_model_weights = 'imagenet' #'imagenet', None. imagenet only nchanls=3
+    init_model_weights = None #'imagenet', None. imagenet only nchanls=3
 
     nP = 6
     nN = 6

test_frozengraph_predictions.py

@@ -0,0 +1,61 @@
+# Load a .pb (Frozen protobuf) and do inference.
+# https://www.dlology.com/blog/how-to-convert-trained-keras-model-to-tensorflow-and-make-prediction/
+
+
+
+import TerminalColors
+tcol = TerminalColors.bcolors()
+
+import tensorflow as tf
+from tensorflow.python.platform import gfile
+from keras import backend as K
+
+import numpy as np
+import time
+
+PB_PATH = 'models.keras/June2019/centeredinput-m1to1-240x320x3__mobilenetv2-block_9_add__K16__allpairloss/'
+PB_FNAME = PB_PATH+'/'+'output_model.pb'
+
+
+#---
+# Load .pb (protobuf file)
+print tcol.OKGREEN , 'READ: ', PB_FNAME, tcol.ENDC
+f = gfile.FastGFile(PB_FNAME, 'rb')
+graph_def = tf.GraphDef()
+# Parses a serialized binary message into the current message.
+graph_def.ParseFromString(f.read())
+f.close()
+
+#---
+# Setup computation graph
+sess = K.get_session()
+sess.graph.as_default()
+# Import a serialized TensorFlow `GraphDef` protocol buffer
+# and place into the current default `Graph`.
+tf.import_graph_def(graph_def)
+
+
+#---
+# Print the graph
+print tcol.OKGREEN, "=== All Nodes in tf.graph ===",tcol.ENDC
+for name in  [n.name for n in tf.get_default_graph().as_graph_def().node]:
+    print name
+print tcol.OKGREEN, "=== END All Nodes in tf.graph ===", tcol.ENDC
+print 'note: The input/output tensors will have the name as opname:0 for example'
+
+
+#---
+# Prediction
+print tcol.OKGREEN, "=== sess.run ===",tcol.ENDC
+softmax_tensor = sess.graph.get_tensor_by_name('import/net_vlad_layer_1/l2_normalize_1:0')
+
+x_test = np.random.random( (1,480,640,3) )
+
+n_inference = 100
+start_t = time.time()
+for _ in range(n_inference): #do 10 inferences
+    predictions = sess.run(softmax_tensor, {'import/input_1:0': x_test})
+end_t = time.time()
+
+print tcol.BOLD, 'x_test.shape=', x_test.shape , '---->' , 'predictions.shape=', predictions.shape, tcol.ENDC
+print n_inference, ' inference took: (ms)', 1000.* (end_t - start_t )

util_keras-h5-model_to-tensorflow-pb_to-nvinfer-uff.py

@@ -300,10 +300,10 @@ def verify_generated_uff_with_tensorrt_uffparser( ufffilename ):
 
     #-----
     # Write UFF
-    convert_to_uff( pb_input_fname=LOG_DIR+'/output_model.pb', uff_output_fname=LOG_DIR+'/output_nvinfer.uff' )
+    #convert_to_uff( pb_input_fname=LOG_DIR+'/output_model.pb', uff_output_fname=LOG_DIR+'/output_nvinfer.uff' )
     # convert_to_uff( pb_input_fname=LOG_DIR+'/output_model_aftersurgery.pb', uff_output_fname=LOG_DIR+'/output_nvinfer.uff' )
 
 
     #-----
     # Try to load UFF with tensorrt
-    verify_generated_uff_with_tensorrt_uffparser( ufffilename=LOG_DIR+'/output_nvinfer.uff' )
+    #verify_generated_uff_with_tensorrt_uffparser( ufffilename=LOG_DIR+'/output_nvinfer.uff' )

util_kerasmodel_edit.py

@@ -16,11 +16,14 @@
 import TerminalColors
 tcol = TerminalColors.bcolors()
 
+import tensorflow as tf
 
-def load_keras_hdf5_model( kerasmodel_h5file, verbose=True  ):
+
+def load_keras_hdf5_model( kerasmodel_h5file, verbose=True, inference_only=False  ):
     """ Loads keras model from a HDF5 file """
     assert os.path.isfile( kerasmodel_h5file ), 'The model weights file doesnot exists or there is a permission issue.'+"kerasmodel_file="+kerasmodel_h5file
-    # K.set_learning_phase(0)
+    if inference_only:
+        K.set_learning_phase(0)
 
     model = keras.models.load_model(kerasmodel_h5file, custom_objects={'NetVLADLayer': NetVLADLayer, 'GhostVLADLayer': GhostVLADLayer}  )
 
@@ -68,15 +71,15 @@ def load_keras_hdf5_model( kerasmodel_h5file, verbose=True  ):
     #---
     # Change Shape
     new_input_shape= (  None, nrows, ncols, model.input.shape[3].value )
-    new_model = change_model_inputshape( model, new_input_shape=new_input_shape )
-
+    new_model = change_model_inputshape( model, new_input_shape=new_input_shape, verbose=True )
+    new_model.summary()
 
 
     #---
     # Model Save
     new_model_fname = '.'.join( kerasmodel_h5file.split( '.' )[0:-1] )+'.%dx%dx%d.h5' %(new_input_shape[1], new_input_shape[2], new_input_shape[3])
     print '====\n====Save new_model to:', tcol.OKBLUE, new_model_fname, tcol.ENDC, '\n===='
-    import code
-    code.interact( local=locals() )
+    # import code
+    # code.interact( local=locals() )
     new_model.save( new_model_fname )
     print tcol.OKGREEN+'====DONE===='+tcol.ENDC

util_kerasmodel_info.py

@@ -79,6 +79,7 @@ def do_inference_on_random_input( model ):
     #---
     # Load HDF5 Keras model
     model = load_keras_hdf5_model( kerasmodel_h5file, verbose=True ) #this
+    model.summary()