I am working with the ssd_mobilenet_v2_coco_2018_03_29 pretrained Tensorflow model. I want to change the input to fixed size, and save it under the saved_model.pb (I am using Neuron Compiler which require this format).
Here is how I change the input Tensor to fixed size:
graph = tf.Graph()
with graph.as_default():
fixed_image_tensor = tf.placeholder(tf.uint8, shape=(None, 300, 300, 3), name='image_tensor')
graph_def = tf.GraphDef()
with tf.io.gfile.GFile(frozen_pb_file, 'rb') as f:
serialized_graph = f.read()
graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(graph_def, name='', input_map={"image_tensor:0": fixed_image_tensor})
And now I save the modified graph to saved_model.pb format by using tf.saved_model.simple_save:
image_tensor = graph.get_tensor_by_name('image_tensor:0')
boxes_tensor = graph.get_tensor_by_name('detection_boxes:0')
scores_tensor = graph.get_tensor_by_name('detection_scores:0')
classes_tensor = graph.get_tensor_by_name('detection_classes:0')
num_detections_tensor = graph.get_tensor_by_name('num_detections:0')
sess = tf.Session(graph=graph)
tf.saved_model.simple_save(
session=sess,
export_dir='model/',
inputs={image_tensor.name: image_tensor},
outputs={
boxes_tensor.name: boxes_tensor,
scores_tensor.name: scores_tensor,
classes_tensor.name: classes_tensor,
num_detections_tensor.name: num_detections_tensor
}
)
The code create the following directory (variables is empty):
|-model/
|---variables/
|---saved_model.pb
The saved_model.pb is only 370 bytes, and must contains no actual information. I also try tf.saved_model.Builder like this and this, but still got the exact same result.
I can still use the sess for inference as usual with no problems. What did I do wrong? Are there any other approaches? I am using Tensorflow 1.15.0.
A bit rearranged code, TF1.13, got 67MBytes *.pb file. Reloaded generated saved_file, input has yours dimensions and all listed outputs:
import tensorflow as tf
frozen_pb_file = "./ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb"
graph = tf.Graph()
with graph.as_default():
fixed_image_tensor = tf.placeholder(tf.uint8, shape=(None, 300, 300, 3), name='image_tensor')
graph_def = tf.GraphDef()
with tf.io.gfile.GFile(frozen_pb_file, 'rb') as f:
serialized_graph = f.read()
graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(graph_def, name='', input_map={"image_tensor:0": fixed_image_tensor})
image_tensor = graph.get_tensor_by_name('image_tensor:0')
boxes_tensor = graph.get_tensor_by_name('detection_boxes:0')
scores_tensor = graph.get_tensor_by_name('detection_scores:0')
classes_tensor = graph.get_tensor_by_name('detection_classes:0')
num_detections_tensor = graph.get_tensor_by_name('num_detections:0')
sess = tf.Session(graph=graph)
file_writer = tf.summary.FileWriter(logdir='log', graph=graph)
tf.saved_model.simple_save(
session=sess,
export_dir='model/',
inputs={image_tensor.name: fixed_image_tensor},
outputs={
boxes_tensor.name: boxes_tensor,
scores_tensor.name: scores_tensor,
classes_tensor.name: classes_tensor,
num_detections_tensor.name: num_detections_tensor
}
)
Related
I'm trying to use a model for OCR and I have this block of code which results in the error:
"AttributeError: module 'tensorflow._api.v2.saved_model' has no attribute 'loader'" which I assume is due to using a TF version 1 construction that is not valid anymore in V2
def load_graph(self, modelFile):
graph = tf.Graph()
# graph_def = tf.compat.v1.GraphDef()
sess = tf.compat.v1.Session(graph=tf.Graph())
Point of failure -> model = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], './model')
graph_def = model.graph_def
with open(modelFile, "rb") as f:
graph_def.ParseFromString(f.read())
with graph.as_default():
tf.import_graph_def(graph_def)
return graph
I have changed the original construction from tf.Session() to tf.compat.v1.Session(), which leads us to this error.
What would be the equivalent for TF V2 that I can use in this case?
EDIT: Previously I had tried this variation which had the same error.
def load_graph(self, modelFile):
graph = tf.Graph()
graph_def = tf.GraphDef()
with open(modelFile, "rb") as f:
graph_def.ParseFromString(f.read())
with graph.as_default():
tf.import_graph_def(graph_def)
return graph
I have tried to freeze my Tensorflow graph and restore it, but when I try and run prediction, I get the error:
You must feed a value for placeholder tensor 'DQNetwork/actions' with dtype float and shape [?,10]
My restoration code is:
sess = tf.Session()
graph = tf.get_default_graph()
with graph.as_default():
with sess.as_default():
GRAPH_PB_PATH = "./frozentensorflowModel.pb"
with gfile.FastGFile(GRAPH_PB_PATH,'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
x_tensor = graph.get_tensor_by_name("DQNetwork/inputs:0")
op_to_restore = graph.get_tensor_by_name("DQNetwork/actions:0")
new_state(cards.copy())
state = game_state.state
feed_dict={x_tensor: state.reshape((1, *state.shape))}
opt = []
opt = sess.run(op_to_restore, feed_dict) # Error throws
predictions = np.argmax(opt, 1)
I defined my DQNetwork inputs like so:
DQNetwork.inputs = tf.placeholder(tf.float32, [None, state_size], name="inputs")
DQNetwork.actions = tf.placeholder(tf.float32, [None, action_size], name="actions")
More info:
>>>op_to_restore
<tf.Tensor 'DQNetwork/actions:0' shape=(?, 10) dtype=float32>
>>>op_to_restore.op
<tf.Operation 'DQNetwork/actions' type=Placeholder>
Training line:
results = sess.run(DQNetwork.output, feed_dict = {DQNetwork.inputs: input_batch})
This may help you:
sess = tf.Session()
graph = tf.get_default_graph()
with graph.as_default():
with sess.as_default():
GRAPH_PB_PATH = "./frozentensorflowModel.pb"
with gfile.FastGFile(GRAPH_PB_PATH,'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
x_tensor = graph.get_tensor_by_name("DQNetwork/inputs:0")
op_to_restore = graph.get_operation_by_name("DQNetwork/actions")
new_state(cards.copy())
state = game_state.state
feed_dict={x_tensor: state.reshape((1, *state.shape))}
opt = []
opt = sess.run(op_to_restore, feed_dict) # Error throws
predictions = np.argmax(opt, 1)
This is what I was suggesting.
I got the point:
feed_dict={x_tensor: state.reshape((1, *state.shape))}
Instead of sess.run(op_to_restore, feed_dict) try op_to_restore.eval(feeddict)
I am trying to export a Tensorflow model so that I can use it in Tensorflow Serving. This is the script that I use:
import os
import tensorflow as tf
trained_checkpoint_prefix = '/home/ubuntu/checkpoint'
export_dir = os.path.join('m', '0')
loaded_graph = tf.Graph()
config=tf.ConfigProto(allow_soft_placement=True)
with tf.Session(graph=loaded_graph, config=config) as sess:
# Restore from checkpoint
loader = tf.train.import_meta_graph(trained_checkpoint_prefix + 'file.meta')
loader.restore(sess, tf.train.latest_checkpoint(trained_checkpoint_prefix))
# Create SavedModelBuilder class
# defines where the model will be exported
export_path_base = "/home/ubuntu/m"
export_path = os.path.join(
tf.compat.as_bytes(export_path_base),
tf.compat.as_bytes(str(0)))
print('Exporting trained model to', export_path)
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
batch_shape = (20, 256, 256, 3)
input_tensor = tf.placeholder(tf.float32, shape=batch_shape, name="X_content")
predictions_tf = tf.placeholder(tf.float32, shape=batch_shape, name='Y_output')
tensor_info_input = tf.saved_model.utils.build_tensor_info(input_tensor)
tensor_info_output = tf.saved_model.utils.build_tensor_info(predictions_tf)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'image': tensor_info_input},
outputs={'output': tensor_info_output},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
'style_image':
prediction_signature,
})
builder.save(as_text=True)
The main issue is the output signature (predictions_tf). In this case, having it set to placeholder, I get an error saying that it's value has to be set when the model is called from gRPC. What should I use instead?
I have tried
predictions_tf = tf.Variable(0, dtype=tf.float32, name="Y_output")
and
predictions_tf = tf.TensorInfo(dtype=tf.float32)
predictions_tf.name = "Y_output"
predictions_tf.dtype = tf.float32
I might misunderstood what you are trying to do, but here you basically create a new placeholder for input and a new placeholder for output.
What I think you should do, is once you loaded the model , you have to get the input and the output tensor of your model in the variables input tensor and prediction_tfusing for example
input_tensor=loaded_graph.get_tensor_by_name('the_name_in_the_loaded_graph:0')
prediction_tf=loaded_graph.get_tensor_by_name('the_pred_name_in_the_loaded_graph:0')
How can I implement the resize function in this construction?
For example, in_image has shape = (845, 594, 3), but I want to resize this image to shape = (299, 299, 3)
def main(_):
with tf.Graph().as_default() as graph:
input_graph = FLAGS.input_graph
saved_model_dir = FLAGS.saved_model_dir
# Read in the export graph
with tf.gfile.FastGFile(input_graph, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
# Define SavedModel Signature (inputs and outputs)
in_image = graph.get_tensor_by_name('input:0')
inputs = {'image_bytes': tf.saved_model.utils.build_tensor_info(in_image)}
out_classes = graph.get_tensor_by_name('InceptionV3/Predictions/Reshape_1:0')
outputs = {'prediction':
tf.saved_model.utils.build_tensor_info(out_classes)}
signature = tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs,
outputs=outputs,
method_name='tensorflow/serving/predict'
)
with tf.Session(graph=graph) as sess:
# Save out the SavedModel.
b = saved_model_builder.SavedModelBuilder(saved_model_dir)
b.add_meta_graph_and_variables(sess,
[tf.saved_model.tag_constants.SERVING],
signature_def_map={'serving_default':
signature})
b.save()
if __name__ == '__main__':
tf.app.run()
I figured it out:
def main(_):
with tf.Graph().as_default() as graph:
input_graph = FLAGS.input_graph
saved_model_dir = FLAGS.saved_model_dir
# Read in the export graph
with tf.gfile.FastGFile(input_graph, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
serialized_tf_example = tf.placeholder(tf.string, name='b64')
jpeg = preprocess_image(serialized_tf_example)
out, = tf.import_graph_def(graph.as_graph_def(),
input_map={'input:0': jpeg},
return_elements = ['InceptionV3/Predictions/Reshape_1:0'])
inputs = {'inputs': tf.saved_model.utils.build_tensor_info(serialized_tf_example)}
outputs = {'prediction': tf.saved_model.utils.build_tensor_info(out)}
signature = tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs,
outputs=outputs,
method_name='tensorflow/serving/predict'
)
with tf.Session(graph=graph) as sess:
# Save out the SavedModel.
b = saved_model_builder.SavedModelBuilder(saved_model_dir)
b.add_meta_graph_and_variables(sess,
[tf.saved_model.tag_constants.SERVING],
signature_def_map={'serving_default': signature})
b.save()
I'm trying to export my retrained inception graph (pb file) for tensorflow serve since I was not able to find any snippets for exporting pb file I had to create my own but obviously I'm doing something wrong since I'm getting "No variables to save" error, I would appreciate any help
Update: after researching more I think I need meta graph to supply variables but retrain.py doesn't give me meta graph, any ideas ?
import tensorflow as tf,sys
label_lines = [line.rstrip() for line
in tf.gfile.GFile("retrained_labels.txt")]
graph = None
with tf.gfile.FastGFile("retrained_graph.pb", 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
graph = tf.import_graph_def(graph_def, name='')
with tf.Session(graph=graph) as sess:
output_tensor = sess.graph.get_tensor_by_name('final_result:0')
input_tensor = sess.graph.get_tensor_by_name('DecodeJpeg/contents:0')
mapping_string = tf.constant( label_lines )
indices = tf.constant( [0,len(label_lines)-1], tf.int64 )
prediction_classes = tf.contrib.lookup.index_to_string( indices, mapping=mapping_string )
export_path = sys.argv[1]
print('Exporting trained model to %s' % export_path)
init_op = tf.group(tf.initialize_all_tables(), name='init_op')
saver = tf.train.Saver(sharded=True)
model_exporter = exporter.Exporter(saver)
model_exporter.init(
sess.graph.as_graph_def(),
init_op=init_op,
default_graph_signature=exporter.classification_signature(
input_tensor=input_tensor,
classes_tensor=prediction_classes,
scores_tensor=output_tensor),
named_graph_signatures={
'inputs': exporter.generic_signature({'images': 'final_result:0'}),
'outputs': exporter.generic_signature({'scores': 'DecodeJpeg/contents:0'})})
model_exporter.export(export_path, tf.constant(1), sess)
print('Done exporting!')