I have a ptocolbuffer file for a VGG model.I need to generate the keras model. Can I use the .pb file to generate Keras model so that I can use the Keras features like summary?
I loaded the .pb file in tf and generate model cofigs and layer weights.
import tensorflow as tf
from tensorflow.python.platform import gfile
from tensorflow.python.framework import tensor_util
GRAPH_PB_PATH = './small_vgg_tf_graph.pb'
with tf.Session() as sess:
print("load graph")
with gfile.FastGFile(GRAPH_PB_PATH,'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def, name='')
graph_nodes=[n for n in graph_def.node]
names = []
for t in graph_nodes:
names.append(t.name)
with open('names.txt','w') as fh:
for i in names:
fh.write(str(i)+'\n')
weight_nodes = [n for n in graph_def.node if n.op == 'Const']
with open('weights.txt','w') as fh:
for n in weight_nodes:
fh.write("Name of the node - %s" % n.name+'\n')
fh.write("Value - " )
fh.write(str(tensor_util.MakeNdarray(n.attr['value'].tensor))+'\n')
with open('ops.txt','w') as fh:
for op in sess.graph.get_operations():
fh.write(str(op)+'\n')
I want to generate Keras like summary file
Related
I followed the website: https://leimao.github.io/blog/Save-Load-Inference-From-TF2-Frozen-Graph/
However, I still do not know how to run inference with frozen_func(see my code below).
Please advise how to run inference using pb file in TensorFlow 2.2. Thanks.
import tensorflow as tf
def wrap_frozen_graph(graph_def, inputs, outputs, print_graph=False):
def _imports_graph_def():
tf.compat.v1.import_graph_def(graph_def, name="")
wrapped_import = tf.compat.v1.wrap_function(_imports_graph_def, [])
import_graph = wrapped_import.graph
print("-" * 50)
print("Frozen model layers: ")
layers = [op.name for op in import_graph.get_operations()]
if print_graph == True:
for layer in layers:
print(layer)
print("-" * 50)
return wrapped_import.prune(
tf.nest.map_structure(import_graph.as_graph_element, inputs),
tf.nest.map_structure(import_graph.as_graph_element, outputs))
# Load frozen graph using TensorFlow 1.x functions
with tf.io.gfile.GFile("/content/drive/My Drive/Model_file/froze_graph.pb", "rb") as f:
graph_def = tf.compat.v1.GraphDef()
loaded = graph_def.ParseFromString(f.read())
# Wrap frozen graph to ConcreteFunctions
frozen_func = wrap_frozen_graph(graph_def=graph_def,
inputs=["wav_data:0"],
outputs=["labels_softmax:0"],
print_graph=True)
You can use tf.graph_util.import_graph_def inside a tf.function to do that. For example, suppose you make a test GraphDef file my_func.pb like this:
import tensorflow as tf
# Test function to make into a GraphDef file
#tf.function
def my_func(x):
return tf.square(x, name='y')
# Get graph
g = my_func.get_concrete_function(tf.TensorSpec(None, tf.float32)).graph
# Write to file
tf.io.write_graph(g, '.', 'my_func.pb', as_text=False)
You can then load it and use it like this:
import tensorflow as tf
from tensorflow.core.framework.graph_pb2 import GraphDef
# Load GraphDef
with open('my_func.pb', 'rb') as f:
gd = GraphDef()
gd.ParseFromString(f.read())
#tf.function
def my_func2(x):
# Ensure the input is a tensor of the right type
x = tf.convert_to_tensor(x, tf.float32)
# Import the graph giving x as input and getting the output y
y = tf.graph_util.import_graph_def(
gd, input_map={'x:0': x}, return_elements=['y:0'])[0]
return y
tf.print(my_func2(2))
# 4
I have exported my model from Google AutoML image classification. I only have a saved_model.pb and no checkpoint or metafiles. Is there a way to convert this saved_model.pb to frozen_inferenc_graph
You can achieve this by the following method with only .pb file.
import tensorflow as tf
from tensorflow.python.platform import gfile
GRAPH_PB_PATH = './model/model_file.pb' #path to your .pb file
with tf.Session(config=config) as sess:
print("load graph")
with gfile.FastGFile(GRAPH_PB_PATH,'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def, name='')
graph_nodes=[n for n in graph_def.node]
Now when you freeze a graph to .pb file your variables are converted to Const type and the weights which were trainable variables would also be stored as Const in .pb file. graph_nodes contains all the nodes in the graph. But we are interested in all the Const type nodes.
wts = [n for n in graph_nodes if n.op=='Const']
Each element of wts is of NodeDef type. It has several attributes such as name, op etc. The values can be extracted as follows -
from tensorflow.python.framework import tensor_util
for n in wts:
print "Name of the node - %s" % n.name
print "Value - "
print tensor_util.MakeNdarray(n.attr['value'].tensor)
Hope this solves your concern.
I'm trying to load an already trained model taken from https://github.com/tensorflow/models/tree/master/official/resnet, but when I try to load the .pb I get an error on ParseFromString method:
import tensorflow as tf
from tensorflow.python.platform import gfile
GRAPH_PB_PATH = '../resnet_v2_fp32_savedmodel_NHWC/1538687283/saved_model.pb'
with tf.gfile.FastGFile(GRAPH_PB_PATH, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
g_in = tf.import_graph_def(graph_def, name="")
sess = tf.Session(graph=g_in)
DecodeError: Error parsing message
What I wrong?
I was having a similar problem, instead of using gfile I use the tf.saved_model.loader.load function like in this post https://stackoverflow.com/a/46547595/4637693:
sess = tf.Session(graph=tf.Graph())
model = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], './model')
graph_def = model.graph_def
I am trying to convert a keras model to use it for predictions on google cloud's ml-engine. I have a pre-trained classifier that takes in a numpy array as input. The normal working data I send to model.predict is named input_data.
I convert it to base 64 and dump it to a json file using the following few lines:
data = {}
data['image_bytes'] = [{'b64':base64.b64encode(input_data.tostring())}]
with open('weights/keras/example.json', 'w') as outfile:
json.dump(data, outfile)
Now, I try to create the TF model from my existing model:
from keras.models import model_from_json
import tensorflow as tf
from keras import backend as K
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import utils
from tensorflow.python.saved_model import tag_constants, signature_constants
from tensorflow.python.saved_model.signature_def_utils_impl import build_signature_def, predict_signature_def
init = tf.global_variables_initializer()
with tf.Session() as sess:
K.set_session(sess)
sess.run(init)
print("Keras model & weights loading...")
K.set_learning_phase(0)
with open(json_file_path, 'r') as file_handle:
model = model_from_json(file_handle.read())
model.load_weights(weight_file_path)
builder = saved_model_builder.SavedModelBuilder(export_path)
raw_byte_strings = tf.placeholder(dtype=tf.string, shape=[None], name='source')
decode = lambda raw_byte_str: tf.decode_raw(raw_byte_str, tf.float32)
input_images = tf.map_fn(decode, raw_byte_strings)
print(input_images)
signature = predict_signature_def(inputs={'image_bytes': input_images},
outputs={'output': model.output})
builder.add_meta_graph_and_variables(
sess=sess,
tags=[tag_constants.SERVING],
signature_def_map={
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature
}
)
builder.save()
When I try to test this locally I get the following error:
ERROR:root:Exception during running the graph: You must feed a value for placeholder tensor 'input_1' with dtype float and shape [?,473,473,3]
[[Node: input_1 = Placeholder[dtype=DT_FLOAT, shape=[?,473,473,3], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Help?
I've been following the TensorFlow for Poets 2 codelab on a model I've trained, and have created a frozen, quantized graph with embedded weights. It's captured in a single file - say my_quant_graph.pb.
Since I can use that graph for inference with the TensorFlow Android inference library just fine, I thought I could do the same with Cloud ML Engine, but it seems it only works on a SavedModel model.
How can I simply convert a frozen/quantized graph in a single pb file to use on ML engine?
It turns out that a SavedModel provides some extra info around a saved graph. Assuming a frozen graph doesn't need assets, then it needs only a serving signature specified.
Here's the python code I ran to convert my graph to a format that Cloud ML engine accepted. Note I only have a single pair of input/output tensors.
import tensorflow as tf
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants
export_dir = './saved'
graph_pb = 'my_quant_graph.pb'
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
with tf.gfile.GFile(graph_pb, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sigs = {}
with tf.Session(graph=tf.Graph()) as sess:
# name="" is important to ensure we don't get spurious prefixing
tf.import_graph_def(graph_def, name="")
g = tf.get_default_graph()
inp = g.get_tensor_by_name("real_A_and_B_images:0")
out = g.get_tensor_by_name("generator/Tanh:0")
sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
tf.saved_model.signature_def_utils.predict_signature_def(
{"in": inp}, {"out": out})
builder.add_meta_graph_and_variables(sess,
[tag_constants.SERVING],
signature_def_map=sigs)
builder.save()
There is sample with multiple outputs nodes:
# Convert PtotoBuf model to saved_model, format for TF Serving
# https://cloud.google.com/ai-platform/prediction/docs/exporting-savedmodel-for-prediction
import shutil
import tensorflow.compat.v1 as tf
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants
export_dir = './1' # TF Serving supports run different versions of same model. So we put current model to '1' folder.
graph_pb = 'frozen_inference_graph.pb'
# Clear out folder
shutil.rmtree(export_dir, ignore_errors=True)
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
with tf.io.gfile.GFile(graph_pb, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sigs = {}
with tf.Session(graph=tf.Graph()) as sess:
# Prepare input and outputs of model
tf.import_graph_def(graph_def, name="")
g = tf.get_default_graph()
image_tensor = g.get_tensor_by_name("image_tensor:0")
num_detections = g.get_tensor_by_name("num_detections:0")
detection_scores = g.get_tensor_by_name("detection_scores:0")
detection_boxes = g.get_tensor_by_name("detection_boxes:0")
detection_classes = g.get_tensor_by_name("detection_classes:0")
sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
tf.saved_model.signature_def_utils.predict_signature_def(
{"input_image": image_tensor},
{ "num_detections": num_detections,
"detection_scores": detection_scores,
"detection_boxes": detection_boxes,
"detection_classes": detection_classes})
builder.add_meta_graph_and_variables(sess,
[tag_constants.SERVING],
signature_def_map=sigs)
builder.save()