Python tensorflow.import_graph_def() Examples
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Example #1
Source File: util.py From ARU-Net with GNU General Public License v2.0 | 8 votes |
def load_graph(frozen_graph_filename): # We load the protobuf file from the disk and parse it to retrieve the # unserialized graph_def with tf.gfile.GFile(frozen_graph_filename, "rb") as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) # Then, we can use again a convenient built-in function to import a graph_def into the # current default Graph with tf.Graph().as_default() as graph: tf.import_graph_def( graph_def, input_map=None, return_elements=None, name="", op_dict=None, producer_op_list=None ) return graph
Example #2
Source File: object_detection_multithreading.py From object_detector_app with MIT License | 7 votes |
def worker(input_q, output_q): # Load a (frozen) Tensorflow model into memory. detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) fps = FPS().start() while True: fps.update() frame = input_q.get() frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) output_q.put(detect_objects(frame_rgb, sess, detection_graph)) fps.stop() sess.close()
Example #3
Source File: utils.py From speech_separation with MIT License | 7 votes |
def load_graph(graph_path,tensorboard=False,**kwargs): ''' :param graph_filename: the path of the pb file :return: tensorflow graph ''' with gfile.FastGFile(graph_path,'rb') as f: graph_def = graph_pb2.GraphDef() graph_def.ParseFromString(f.read()) with tf.Graph().as_default() as graph: tf.import_graph_def(graph_def,name="") if tensorboard: writer = tf.summary.FileWriter("log/") writer.add_graph(graph) return graph
Example #4
Source File: retrain.py From tensorflow-image-detection with MIT License | 6 votes |
def create_inception_graph(): """"Creates a graph from saved GraphDef file and returns a Graph object. Returns: Graph holding the trained Inception network, and various tensors we'll be manipulating. """ with tf.Graph().as_default() as graph: model_filename = os.path.join( FLAGS.model_dir, 'classify_image_graph_def.pb') with gfile.FastGFile(model_filename, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = ( tf.import_graph_def(graph_def, name='', return_elements=[ BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME, RESIZED_INPUT_TENSOR_NAME])) return graph, bottleneck_tensor, jpeg_data_tensor, resized_input_tensor
Example #5
Source File: facenet.py From TNT with GNU General Public License v3.0 | 6 votes |
def load_model(model, input_map=None): # Check if the model is a model directory (containing a metagraph and a checkpoint file) # or if it is a protobuf file with a frozen graph model_exp = os.path.expanduser(model) if (os.path.isfile(model_exp)): print('Model filename: %s' % model_exp) with gfile.FastGFile(model_exp,'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) tf.import_graph_def(graph_def, input_map=input_map, name='') else: print('Model directory: %s' % model_exp) meta_file, ckpt_file = get_model_filenames(model_exp) print('Metagraph file: %s' % meta_file) print('Checkpoint file: %s' % ckpt_file) saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file), input_map=input_map) saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file))
Example #6
Source File: facenet.py From TNT with GNU General Public License v3.0 | 6 votes |
def load_model(model, input_map=None): # Check if the model is a model directory (containing a metagraph and a checkpoint file) # or if it is a protobuf file with a frozen graph model_exp = os.path.expanduser(model) if (os.path.isfile(model_exp)): print('Model filename: %s' % model_exp) with gfile.FastGFile(model_exp,'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) tf.import_graph_def(graph_def, input_map=input_map, name='') else: print('Model directory: %s' % model_exp) meta_file, ckpt_file = get_model_filenames(model_exp) print('Metagraph file: %s' % meta_file) print('Checkpoint file: %s' % ckpt_file) saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file), input_map=input_map) saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file))
Example #7
Source File: facenet.py From TNT with GNU General Public License v3.0 | 6 votes |
def load_model(model, input_map=None): # Check if the model is a model directory (containing a metagraph and a checkpoint file) # or if it is a protobuf file with a frozen graph model_exp = os.path.expanduser(model) if (os.path.isfile(model_exp)): print('Model filename: %s' % model_exp) with gfile.FastGFile(model_exp,'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) tf.import_graph_def(graph_def, input_map=input_map, name='') else: print('Model directory: %s' % model_exp) meta_file, ckpt_file = get_model_filenames(model_exp) print('Metagraph file: %s' % meta_file) print('Checkpoint file: %s' % ckpt_file) saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file), input_map=input_map) saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file))
Example #8
Source File: generate_detections.py From deep_sort with GNU General Public License v3.0 | 6 votes |
def __init__(self, checkpoint_filename, input_name="images", output_name="features"): self.session = tf.Session() with tf.gfile.GFile(checkpoint_filename, "rb") as file_handle: graph_def = tf.GraphDef() graph_def.ParseFromString(file_handle.read()) tf.import_graph_def(graph_def, name="net") self.input_var = tf.get_default_graph().get_tensor_by_name( "net/%s:0" % input_name) self.output_var = tf.get_default_graph().get_tensor_by_name( "net/%s:0" % output_name) assert len(self.output_var.get_shape()) == 2 assert len(self.input_var.get_shape()) == 4 self.feature_dim = self.output_var.get_shape().as_list()[-1] self.image_shape = self.input_var.get_shape().as_list()[1:]
Example #9
Source File: build.py From Automatic-Identification-and-Counting-of-Blood-Cells with GNU General Public License v3.0 | 6 votes |
def build_from_pb(self): with tf.gfile.FastGFile(self.FLAGS.pbLoad, "rb") as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) tf.import_graph_def( graph_def, name="" ) with open(self.FLAGS.metaLoad, 'r') as fp: self.meta = json.load(fp) self.framework = create_framework(self.meta, self.FLAGS) # Placeholders self.inp = tf.get_default_graph().get_tensor_by_name('input:0') self.feed = dict() # other placeholders self.out = tf.get_default_graph().get_tensor_by_name('output:0') self.setup_meta_ops()
Example #10
Source File: detect_face.py From face-detection-ssd-mobilenet with Apache License 2.0 | 6 votes |
def __init__(self, PATH_TO_CKPT): """Tensorflow detector """ self.detection_graph = tf.Graph() with self.detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') with self.detection_graph.as_default(): config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(graph=self.detection_graph, config=config) as self.sess: self.windowNotSet = True
Example #11
Source File: convertmodel.py From DmsMsgRcg with Apache License 2.0 | 6 votes |
def __init__(self, config, graph, model_scope, model_dir, model_file): self.config = config frozen_model = os.path.join(model_dir, model_file) with tf.gfile.GFile(frozen_model, "rb") as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) # This model_scope adds a prefix to all the nodes in the graph tf.import_graph_def(graph_def, input_map=None, return_elements=None, name="{}/".format(model_scope)) # Uncomment the two lines below to look for the names of all the operations in the graph # for op in graph.get_operations(): # print(op.name) # Using the lines commented above to look for the tensor name of the input node # Or you can figure it out in your original model, if you explicitly named it. self.input_tensor = graph.get_tensor_by_name("{}/input_1:0".format(model_scope)) self.output_tensor = graph.get_tensor_by_name("{}/s1_output0:0".format(model_scope))
Example #12
Source File: model.py From tensorprob with MIT License | 6 votes |
def _rewrite_graph(self, transform): input_map = {k.name: v for k, v in transform.items()} # Modify the input dictionary to replace variables which have been # superseded with the use of combinators for k, v in self._silently_replace.items(): input_map[k.name] = self._observed[v] with self.session.graph.as_default(): try: tf.import_graph_def( self._model_graph.as_graph_def(), input_map=input_map, name='added', ) except ValueError: # Ignore errors that ocour when the input_map tries to # rewrite a variable that isn't present in the graph pass
Example #13
Source File: object_detection_app.py From object_detector_app with MIT License | 6 votes |
def worker(input_q, output_q): # Load a (frozen) Tensorflow model into memory. detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) fps = FPS().start() while True: fps.update() frame = input_q.get() frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) output_q.put(detect_objects(frame_rgb, sess, detection_graph)) fps.stop() sess.close()
Example #14
Source File: retrain.py From diabetic-retinopathy-screening with GNU General Public License v3.0 | 6 votes |
def create_inception_graph(): """"Creates a graph from saved GraphDef file and returns a Graph object. Returns: Graph holding the trained Inception network, and various tensors we'll be manipulating. """ with tf.Session() as sess: model_filename = os.path.join( FLAGS.model_dir, 'classify_image_graph_def.pb') with gfile.FastGFile(model_filename, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = ( tf.import_graph_def(graph_def, name='', return_elements=[ BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME, RESIZED_INPUT_TENSOR_NAME])) return sess.graph, bottleneck_tensor, jpeg_data_tensor, resized_input_tensor
Example #15
Source File: model_ops.py From speech_separation with MIT License | 6 votes |
def load_graph(graph_path,tensorboard=False,**kwargs): ''' :param graph_filename: the path of the pb file :return: tensorflow graph ''' with gfile.FastGFile(graph_path,'rb') as f: graph_def = graph_pb2.GraphDef() graph_def.ParseFromString(f.read()) with tf.Graph().as_default() as graph: tf.import_graph_def(graph_def,name="") if tensorboard: writer = tf.summary.FileWriter("log/") writer.add_graph(graph) return graph
Example #16
Source File: input.py From spark-deep-learning with Apache License 2.0 | 6 votes |
def fromGraphDef(cls, graph_def, feed_names, fetch_names): """ Construct a TFInputGraph from a tf.GraphDef object. :param graph_def: :py:class:`tf.GraphDef`, a serializable object containing the topology and computation units of the TensorFlow graph. :param feed_names: list, names of the input tensors. :param fetch_names: list, names of the output tensors. """ assert isinstance(graph_def, tf.GraphDef), \ ('expect tf.GraphDef type but got', type(graph_def)) graph = tf.Graph() with tf.Session(graph=graph) as sess: tf.import_graph_def(graph_def, name='') return _build_with_feeds_fetches(sess=sess, graph=graph, feed_names=feed_names, fetch_names=fetch_names)
Example #17
Source File: detector_utils.py From Emojinator with MIT License | 6 votes |
def load_inference_graph(): # load frozen tensorflow model into memory print("> ====== loading HAND frozen graph into memory") detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) print("> ====== Hand Inference graph loaded.") return detection_graph, sess # draw the detected bounding boxes on the images # You can modify this to also draw a label.
Example #18
Source File: generate_detections.py From multi-object-tracking with GNU General Public License v3.0 | 6 votes |
def __init__(self, checkpoint_filename, input_name="images", output_name="features"): self.session = tf.Session() with tf.gfile.GFile(checkpoint_filename, "rb") as file_handle: graph_def = tf.GraphDef() graph_def.ParseFromString(file_handle.read()) tf.import_graph_def(graph_def, name="net") self.input_var = tf.get_default_graph().get_tensor_by_name( "net/%s:0" % input_name) self.output_var = tf.get_default_graph().get_tensor_by_name( "net/%s:0" % output_name) assert len(self.output_var.get_shape()) == 2 assert len(self.input_var.get_shape()) == 4 self.feature_dim = self.output_var.get_shape().as_list()[-1] self.image_shape = self.input_var.get_shape().as_list()[1:]
Example #19
Source File: test_exportPb.py From R2CNN_Faster-RCNN_Tensorflow with MIT License | 6 votes |
def load_graph(frozen_graph_file): # we parse the graph_def file with tf.gfile.GFile(frozen_graph_file, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) # we load the graph_def in the default graph with tf.Graph().as_default() as graph: tf.import_graph_def(graph_def, input_map=None, return_elements=None, name="", op_dict=None, producer_op_list=None) return graph
Example #20
Source File: inference.py From CycleGAN-TensorFlow with MIT License | 6 votes |
def inference(): graph = tf.Graph() with graph.as_default(): with tf.gfile.FastGFile(FLAGS.input, 'rb') as f: image_data = f.read() input_image = tf.image.decode_jpeg(image_data, channels=3) input_image = tf.image.resize_images(input_image, size=(FLAGS.image_size, FLAGS.image_size)) input_image = utils.convert2float(input_image) input_image.set_shape([FLAGS.image_size, FLAGS.image_size, 3]) with tf.gfile.FastGFile(FLAGS.model, 'rb') as model_file: graph_def = tf.GraphDef() graph_def.ParseFromString(model_file.read()) [output_image] = tf.import_graph_def(graph_def, input_map={'input_image': input_image}, return_elements=['output_image:0'], name='output') with tf.Session(graph=graph) as sess: generated = output_image.eval() with open(FLAGS.output, 'wb') as f: f.write(generated)
Example #21
Source File: export_tflite_ssd_graph_lib_test.py From vehicle_counting_tensorflow with MIT License | 6 votes |
def _import_graph_and_run_inference(self, tflite_graph_file, num_channels=3): """Imports a tflite graph, runs single inference and returns outputs.""" graph = tf.Graph() with graph.as_default(): graph_def = tf.GraphDef() with tf.gfile.Open(tflite_graph_file) as f: graph_def.ParseFromString(f.read()) tf.import_graph_def(graph_def, name='') input_tensor = graph.get_tensor_by_name('normalized_input_image_tensor:0') box_encodings = graph.get_tensor_by_name('raw_outputs/box_encodings:0') class_predictions = graph.get_tensor_by_name( 'raw_outputs/class_predictions:0') with self.test_session(graph) as sess: [box_encodings_np, class_predictions_np] = sess.run( [box_encodings, class_predictions], feed_dict={input_tensor: np.random.rand(1, 10, 10, num_channels)}) return box_encodings_np, class_predictions_np
Example #22
Source File: predict.py From Custom-vision-service-iot-edge-raspberry-pi with MIT License | 6 votes |
def initialize(): print('Loading model...',end=''), with open(filename, 'rb') as f: graph_def.ParseFromString(f.read()) tf.import_graph_def(graph_def, name='') # Retrieving 'network_input_size' from shape of 'input_node' with tf.compat.v1.Session() as sess: input_tensor_shape = sess.graph.get_tensor_by_name(input_node).shape.as_list() assert len(input_tensor_shape) == 4 assert input_tensor_shape[1] == input_tensor_shape[2] global network_input_size network_input_size = input_tensor_shape[1] print('Success!') print('Loading labels...', end='') with open(labels_filename, 'rt') as lf: global labels labels = [l.strip() for l in lf.readlines()] print(len(labels), 'found. Success!')
Example #23
Source File: build.py From Traffic-Signs-and-Object-Detection with GNU General Public License v3.0 | 6 votes |
def build_from_pb(self): with tf.gfile.FastGFile(self.FLAGS.pbLoad, "rb") as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) tf.import_graph_def( graph_def, name="" ) with open(self.FLAGS.metaLoad, 'r') as fp: self.meta = json.load(fp) self.framework = create_framework(self.meta, self.FLAGS) # Placeholders self.inp = tf.get_default_graph().get_tensor_by_name('input:0') self.feed = dict() # other placeholders self.out = tf.get_default_graph().get_tensor_by_name('output:0') self.setup_meta_ops()
Example #24
Source File: predict-amd64.py From Custom-vision-service-iot-edge-raspberry-pi with MIT License | 6 votes |
def initialize(): print('Loading model...',end=''), with open(filename, 'rb') as f: graph_def.ParseFromString(f.read()) tf.import_graph_def(graph_def, name='') # Retrieving 'network_input_size' from shape of 'input_node' with tf.compat.v1.Session() as sess: input_tensor_shape = sess.graph.get_tensor_by_name(input_node).shape.as_list() assert len(input_tensor_shape) == 4 assert input_tensor_shape[1] == input_tensor_shape[2] global network_input_size network_input_size = input_tensor_shape[1] print('Success!') print('Loading labels...', end='') with open(labels_filename, 'rt') as lf: global labels labels = [l.strip() for l in lf.readlines()] print(len(labels), 'found. Success!')
Example #25
Source File: retrain.py From powerai-transfer-learning with Apache License 2.0 | 6 votes |
def create_inception_graph(): """"Creates a graph from saved GraphDef file and returns a Graph object. Returns: Graph holding the trained Inception network, and various tensors we'll be manipulating. """ with tf.Graph().as_default() as graph: model_filename = os.path.join( FLAGS.model_dir, 'classify_image_graph_def.pb') with gfile.FastGFile(model_filename, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = ( tf.import_graph_def(graph_def, name='', return_elements=[ BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME, RESIZED_INPUT_TENSOR_NAME])) return graph, bottleneck_tensor, jpeg_data_tensor, resized_input_tensor
Example #26
Source File: test_import.py From spark-deep-learning with Apache License 2.0 | 6 votes |
def _check_output(gin, tf_input, expected): """ Takes a TFInputGraph object (assumed to have the input and outputs of the given names above) and compares the outcome against some expected outcome. """ graph = tf.Graph() graph_def = gin.graph_def with tf.Session(graph=graph) as sess: tf.import_graph_def(graph_def, name="") tgt_feed = tfx.get_tensor(_tensor_input_name, graph) tgt_fetch = tfx.get_tensor(_tensor_output_name, graph) # Run on the testing target tgt_out = sess.run(tgt_fetch, feed_dict={tgt_feed: tf_input}) # Working on integers, the calculation should be exact assert np.all(tgt_out == expected), (tgt_out, expected) # TODO: we could factorize with _check_output, but this is not worth the time doing it.
Example #27
Source File: test_import.py From spark-deep-learning with Apache License 2.0 | 6 votes |
def _check_output_2(gin, tf_input1, tf_input2, expected): """ Takes a TFInputGraph object (assumed to have the input and outputs of the given names above) and compares the outcome against some expected outcome. """ graph = tf.Graph() graph_def = gin.graph_def with tf.Session(graph=graph) as sess: tf.import_graph_def(graph_def, name="") tgt_feed1 = tfx.get_tensor(_tensor_input_name, graph) tgt_feed2 = tfx.get_tensor(_tensor_input_name_2, graph) tgt_fetch = tfx.get_tensor(_tensor_output_name, graph) # Run on the testing target tgt_out = sess.run(tgt_fetch, feed_dict={tgt_feed1: tf_input1, tgt_feed2: tf_input2}) # Working on integers, the calculation should be exact assert np.all(tgt_out == expected), (tgt_out, expected)
Example #28
Source File: build.py From Traffic_sign_detection_YOLO with MIT License | 6 votes |
def build_from_pb(self): with tf.gfile.FastGFile(self.FLAGS.pbLoad, "rb") as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) tf.import_graph_def( graph_def, name="" ) with open(self.FLAGS.metaLoad, 'r') as fp: self.meta = json.load(fp) self.framework = create_framework(self.meta, self.FLAGS) # Placeholders self.inp = tf.get_default_graph().get_tensor_by_name('input:0') self.feed = dict() # other placeholders self.out = tf.get_default_graph().get_tensor_by_name('output:0') self.setup_meta_ops()
Example #29
Source File: label_image.py From powerai-transfer-learning with Apache License 2.0 | 5 votes |
def load_graph(filename): """Unpersists graph from file as default graph.""" with tf.gfile.FastGFile(filename, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) tf.import_graph_def(graph_def, name='')
Example #30
Source File: tf_tensor.py From spark-deep-learning with Apache License 2.0 | 5 votes |
def _get_placeholder_types(self, user_graph_def): """ Returns a list of placeholder type enums for the input nodes """ user_graph = tf.Graph() with user_graph.as_default(): # Load user-specified graph into memory, then get the data type of each input node tf.import_graph_def(user_graph_def, name="") res = [] for _, tensor_name in self.getInputMapping(): placeholder_type = tfx.get_tensor(tensor_name, user_graph).dtype.as_datatype_enum res.append(placeholder_type) return res