I am using a graph file which i doesn't know the name of the variables on this graph model
this files i have :-
training_model_saved_model.pb
variables
training_model_variables_variables.data-00000-of-00001
training_model_variables_variables.index
i started by this code but i can't get the variable names so i can set them
import tensorflow as tf
import sys
from tensorflow.python.platform import gfile
from tensorflow.core.protobuf import saved_model_pb2
from tensorflow.python.util import compat
with tf.Session() as sess:
model_filename ='training_model_saved_model.pb'
with gfile.FastGFile(model_filename, 'rb') as f:
data = compat.as_bytes(f.read())
sm = saved_model_pb2.SavedModel()
sm.ParseFromString(data)
#print(sm)
if 1 != len(sm.meta_graphs):
print('More than one graph found. Not sure which to write')
sys.exit(1)
graph_def = tf.GraphDef()
#graph_def.ParseFromString(sm.meta_graphs[0])
x,y,z = tf.import_graph_def(sm.meta_graphs[0].graph_def,return_elements=['data/inputs:0',
'output/network_activation:0',
'data/correct_outputs:0'],
name='')
LOGDIR='Log_dir'
train_writer = tf.summary.FileWriter(LOGDIR)
train_writer.add_graph(sess.graph)
error return is expected
Traceback (most recent call last):
File "execute_model.py", line 24, in <module>
'data/correct_outputs:0'],name='')
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/importer.py", line 526, in import_graph_def
'Requested return_element %r not found in graph_def.' % name)
ValueError: Requested return_element 'data/inputs:0' not found in graph_def.
any help , thanks in advance
Related
I am currently trying to download data from the torchtext.datasets module and it is not working.
Here is the following code that I have written (taken from https://analyticsindiamag.com/multi-class-text-classification-in-pytorch-using-torchtext/):
import torch
import torchtext
from torchtext.datasets import text_classification
import os
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import time
from torch.utils.data.dataset import random_split
import re
from torchtext.data.utils import ngrams_iterator
from torchtext.data.utils import get_tokenizer
ngrams = 2
batch_size = 16
if not os.path.isdir('./.data'):
os.mkdir('./.data')
train_dataset, test_dataset = text_classification.DATASETS['DBpedia'](root='./.data', ngrams=ngrams, vocab=None)
It produces the following error:
Traceback (most recent call last):
File "/Users/aidanpayne/Desktop/Scripts/Python/Neural Networks/text_classification_model.py", line 19, in <module>
train_dataset, test_dataset = text_classification.DATASETS['DBpedia'](root='./.data', ngrams=ngrams, vocab=None)
File "/Users/aidanpayne/opt/anaconda3/lib/python3.8/site-packages/torchtext/datasets/text_classification.py", line 237, in DBpedia
return _setup_datasets(*(("DBpedia",) + args), **kwargs)
File "/Users/aidanpayne/opt/anaconda3/lib/python3.8/site-packages/torchtext/datasets/text_classification.py", line 117, in _setup_datasets
dataset_tar = download_from_url(URLS[dataset_name], root=root)
File "/Users/aidanpayne/opt/anaconda3/lib/python3.8/site-packages/torchtext/utils.py", line 100, in download_from_url
return _process_response(response, root, filename)
File "/Users/aidanpayne/opt/anaconda3/lib/python3.8/site-packages/torchtext/utils.py", line 53, in _process_response
d = r.headers['content-disposition']
File "/Users/aidanpayne/opt/anaconda3/lib/python3.8/site-packages/requests/structures.py", line 54, in __getitem__
return self._store[key.lower()][1]
KeyError: 'content-disposition'
If anyone can help, that would be great!
I am trying to open an.h5 file, but experiencing an OS error.
import sys
sys.path.append('..')
from unet3d.training import load_old_model
import tables
from train_model import config
model_file=config["model_file"] #config["model_file"] = os.path.abspath("mc_seg_model.h5")
hdf5_file=config["val_data_file"] #config['val_data_file'] = os.path.abspath("../data/val_data.h5")
model = load_old_model(model_file)
load_model function is as follows:
import math
from functools import partial
import pdb
from keras import backend as K
from keras.callbacks import ModelCheckpoint, CSVLogger, LearningRateScheduler, ReduceLROnPlateau, EarlyStopping
from keras.models import load_model
import tensorflow_addons as tfa
def load_old_model(model_file):
# pdb.set_trace()
print("Loading pre-trained model")
custom_objects = {'dice_coefficient_loss': dice_coefficient_loss, 'dice_coefficient': dice_coefficient,
'weighted_dice_coefficient': weighted_dice_coefficient,
'weighted_dice_coefficient_loss': weighted_dice_coefficient_loss}
try:
#from keras_contrib.layers import InstanceNormalization
from tfa.layers import InstanceNormalization
custom_objects["InstanceNormalization"] = InstanceNormalization
except ImportError:
pass
try:
return load_model(model_file, custom_objects=custom_objects)
except ValueError as error:
if 'InstanceNormalization' in str(error):
raise ValueError(str(error) + "\n\nPlease install keras-contrib to use InstanceNormalization:\n"
"'pip install git+https://www.github.com/keras-team/keras-contrib.git'")
else:
raise error
When I try to load the model, it throws the following OS error and it is an 'Input/output error'.
2021-06-16 14:31:38.354199: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
Traceback (most recent call last):
File "draft.py", line 35, in <module>
model = load_old_model(model_file)
File "../unet3d/training.py", line 50, in load_old_model
return load_model(model_file, custom_objects=custom_objects)
File "/share/apps/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/saving/save.py", line 182, in load_model
return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
File "/share/apps/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/saving/hdf5_format.py", line 173, in load_model_from_hdf5
model_config = f.attrs.get('model_config')
File "/share/apps/anaconda3/lib/python3.7/_collections_abc.py", line 660, in get
return self[key]
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "/share/apps/anaconda3/lib/python3.7/site-packages/h5py/_hl/attrs.py", line 81, in __getitem__
attr.read(arr, mtype=htype)
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "h5py/h5a.pyx", line 355, in h5py.h5a.AttrID.read
File "h5py/_proxy.pyx", line 58, in h5py._proxy.attr_rw
OSError: Unable to read attribute (file read failed: time = Wed Jun 16 14:31:42 2021
, filename = '/data/kfernando/brats20/demo_task3_mcmc/mc_seg_model.h5', file descriptor = 4, errno = 5, error message = 'Input/output error', buf = 0x56126c096440, total read size = 30352, bytes this sub-read = 30352, bytes actually read = 18446744073709551615, offset = 16384)
Can someone please tell me what is causing this error?
Based on your comments about successful h5py open/close, it appears you have a valid HDF5 file. There are 2 more issues to investigate: 1) problems reading attribute data, or 2) errors in TensorFlow load_model() function. I can't help with TF. However here is a bit of code to recursively descend the data hierarchy and output all attributes and values. See below:
def get_all_attrs(name, h5_obj):
if isinstance(h5_obj,h5py.Group):
print('\n{} is a Group'.format(name))
elif isinstance(h5_obj,h5py.Dataset):
print('\n{} is a Dataset'.format(name))
print('number of attributes:',len( h5_obj.attrs.keys() ))
for k in h5_obj.attrs.keys():
print('{} => {}'.format(k, h5_obj.attrs[k]))
with h5py.File(file_path, 'r') as h5r:
print('number of root level attributes:',len( h5r.attrs.keys() ))
for k in h5r.attrs.keys():
print('{} => {}'.format(k, h5r.attrs[k]))
h5r.visititems(get_all_attrs)
Run this with your TF file. It might find a error reading one of the attributes. Example output from my test file looks like this:
number of root level attributes: 2
OS => Windows
User => Me
Base_Group is a Group
number of attributes: 2
Date => today
Time => now
Base_Group/default is a Dataset
number of attributes: 2
attr1 => 1.0
attr2 => 22.2
Group1 is a Group
number of attributes: 0
Group1/default1 is a Dataset
number of attributes: 0
This should help determine the source of the error. If h5py can read the attributes, you need to investigate TF load_data() function. If you get an error reading the attributes....well, that's your problem, but I don't know how to identify the root cause.
for my project I'm trying to do inference based on my trained model stored in saved_model.pb. I doubt that the mistake is due to my code you can see over here but more likely is due to an installation problem:
from PIL import Image
import numpy as np
import scipy
from scipy import misc
import matplotlib.pyplot as plt
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
with tf.Graph().as_default() as graph: # Set default graph as graph
with tf.Session() as sess:
# Load the graph in graph_def
print("load graph")
# We load the protobuf file from the disk and parse it to retrive the unserialized graph_drf
with gfile.FastGFile("saved_model.pb",'rb') as f:
from scipy.io import wavfile
samplerate, data = wavfile.read('sound.wav')
# Set FCN graph to the default graph
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
# Import a graph_def into the current default Graph (In this case, the weights are (typically) embedded in the graph)
tf.import_graph_def(
graph_def,
input_map=None,
return_elements=None,
name="",
op_dict=None,
producer_op_list=None
)
# Print the name of operations in the session
for op in graph.get_operations():
print("Operation Name :",op.name) # Operation name
print("Tensor Stats :",str(op.values())) # Tensor name
# INFERENCE Here
l_input = graph.get_tensor_by_name('Inputs/fifo_queue_Dequeue:0') # Input Tensor
l_output = graph.get_tensor_by_name('upscore32/conv2d_transpose:0') # Output Tensor
print("Shape of input : ", tf.shape(l_input))
f.global_variables_initializer()
# Run model on single image
Session_out = sess.run( m_output, feed_dict = {m_input : data} )
print("Predicted class:", class_names[Session_out[0].argmax()] )
The traceback is the following
Traceback (most recent call last):
File "/home/pi/model_inference/test.py", line 11, in <module>
graph_def.ParseFromString(f.read())
File "/usr/local/lib/python3.7/dist-packages/google/protobuf/message.py", line 199, in ParseFromString
return self.MergeFromString(serialized)
File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/python_message.py", line 1145, in MergeFromString
if self._InternalParse(serialized, 0, length) != length:
File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/python_message.py", line 1212, in InternalParse
pos = field_decoder(buffer, new_pos, end, self, field_dict)
File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/decoder.py", line 754, in DecodeField
if value._InternalParse(buffer, pos, new_pos) != new_pos:
File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/python_message.py", line 1212, in InternalParse
pos = field_decoder(buffer, new_pos, end, self, field_dict)
File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/decoder.py", line 733, in DecodeRepeatedField
if value.add()._InternalParse(buffer, pos, new_pos) != new_pos:
File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/python_message.py", line 1212, in InternalParse
pos = field_decoder(buffer, new_pos, end, self, field_dict)
File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/decoder.py", line 888, in DecodeMap
if submsg._InternalParse(buffer, pos, new_pos) != new_pos:
File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/python_message.py", line 1199, in InternalParse
buffer, new_pos, wire_type) # pylint: disable=protected-access
File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/decoder.py", line 989, in _DecodeUnknownField
(data, pos) = _DecodeUnknownFieldSet(buffer, pos)
File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/decoder.py", line 968, in _DecodeUnknownFieldSet
(data, pos) = _DecodeUnknownField(buffer, pos, wire_type)
File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/decoder.py", line 993, in _DecodeUnknownField
raise _DecodeError('Wrong wire type in tag.')
google.protobuf.message.DecodeError: Wrong wire type in tag.
Important to note is that I'm trying this out on a raspberry pi v4 (thus linux running on it). I would be glad about any hint what to do.
Thanks in advance!
Try using tf.saved_model.load(path) where path is the path to your saved model's folder containing the assets and variables folder.follow this link to the tensorflow object-detection api inference tutorial.
It looks like your file "saved_model.pb" is not a saved (wireformat) protobuffer of the message type GraphDef. Maybe you can look how that was saved and find some instructions on how to load it back? Just guessing from the name, can it be a keras model and you have to use tf.keras.models.load_model?
When I run tensor.py it tell me that "ImportError: No module named freezegraph"
Does it means I should create "freezgraph" by myself? sorry,I'm confused,looking forward for your introduction~
Here is the code:
#!/usr/bin/python
import os
import tensorflow as tf
import pmt
import freezegraph
def save_graph(sess,output_path,checkpoint,checkpoint_state_name,input_graph_name,output_graph_name,outname):
checkpoint_prefix = os.path.join(output_path,checkpoint)
# defaults to saving all variables
saver = tf.train.Saver(tf.all_variables())
saver.save(sess, checkpoint_prefix, global_step=0,latest_filename=checkpoint_state_name)
tf.train.write_graph(sess.graph.as_graph_def(),output_path,
input_graph_name)
# We save out the graph to disk, and then call the const conversion
# routine.
input_graph_path = os.path.join(output_path, input_graph_name)
input_saver_def_path = ""
input_binary = False
input_checkpoint_path = checkpoint_prefix + "-0"
output_node_names = outname
restore_op_name = "save/restore_all"
filename_tensor_name = "save/Const:0"
output_graph_path = os.path.join(output_path, output_graph_name)
clear_devices = False
freezegraph.freeze_graph(input_graph_path, input_saver_def_path,
input_binary, input_checkpoint_path,
output_node_names, restore_op_name,
filename_tensor_name, output_graph_path,clear_devices, "")
def load_graph(output_graph_path,inp,out,ckpt_path=""):
with tf.Graph().as_default():
output_graph_def = tf.GraphDef()
with tf.Session() as sess:
with open(output_graph_path, "rb") as f:
output_graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(output_graph_def, name="")
n_input = sess.graph.get_tensor_by_name(inp)
output = sess.graph.get_tensor_by_name(out)
return (sess,n_input,output)
I change the "import freezegraph " to "from tensorflow.python.tools import freeze_graph" then I get new errors:
File "/home/imatrix/chrisruk/cnn-master/train_cnn.py", line 46, in <module>
save_graph(sess,"/tmp/","saved_checkpoint","checkpoint_state","input_graph.pb","output_graph.pb","out/Softmax")
File "/home/imatrix/ chrisruk/cnn-master/tensor.py", line 35, in save_graph
filename_tensor_name, output_graph_path,clear_devices, "")
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/tools/freeze_graph.py", line 103, in freeze_graph
_ = importer.import_graph_def(input_graph_def, name="")
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/importer.py", line 308, in import_graph_def
op_def=op_def)
File "/usr/local/lib/python2.7/dist-pack`enter code here`ages/tensorflow/python/framework/ops.py", line 2339, in create_op
self._add_op(ret)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2033, in _add_op
"is already used" % op.name)
ValueError: cannot add op with name Crossentropy/Mean/moving_avg as that name is already used
I have a method to convert binary wire format to human readable format but I cannot do the inverse of this
import tensorflow as tf
from tensorflow.python.platform import gfile
def converter(filename):
with gfile.FastGFile(filename,'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
tf.train.write_graph(graph_def, 'pbtxt/', 'protobuf.pb', as_text=True)
return
I just have to type the file name for this and it works. But on doing the opposite i get
File "pb_to_pbtxt.py", line 16, in <module>
converter('protobuf.pb') # here you can write the name of the file to be converted
File "pb_to_pbtxt.py", line 11, in converter
graph_def.ParseFromString(f.read())
File "/usr/local/lib/python2.7/dist-packages/google/protobuf/message.py", line 185, in ParseFromString
self.MergeFromString(serialized)
File "/usr/local/lib/python2.7/dist-packages/google/protobuf/internal/python_message.py", line 1008, in MergeFromString
if self._InternalParse(serialized, 0, length) != length:
File "/usr/local/lib/python2.7/dist-packages/google/protobuf/internal/python_message.py", line 1034, in InternalParse
new_pos = local_SkipField(buffer, new_pos, end, tag_bytes)
File "/usr/local/lib/python2.7/dist-packages/google/protobuf/internal/decoder.py", line 868, in SkipField
return WIRETYPE_TO_SKIPPER[wire_type](buffer, pos, end)
File "/usr/local/lib/python2.7/dist-packages/google/protobuf/internal/decoder.py", line 838, in _RaiseInvalidWireType
raise _DecodeError('Tag had invalid wire type.')
You can perform the reverse translation using the google.protobuf.text_format module:
import tensorflow as tf
from google.protobuf import text_format
def convert_pbtxt_to_graphdef(filename):
"""Returns a `tf.GraphDef` proto representing the data in the given pbtxt file.
Args:
filename: The name of a file containing a GraphDef pbtxt (text-formatted
`tf.GraphDef` protocol buffer data).
Returns:
A `tf.GraphDef` protocol buffer.
"""
with tf.gfile.FastGFile(filename, 'r') as f:
graph_def = tf.GraphDef()
file_content = f.read()
# Merges the human-readable string in `file_content` into `graph_def`.
text_format.Merge(file_content, graph_def)
return graph_def
You can use tf.Graph.as_graph_def() and then Protobuf's SerializeToString() like so:
proto_graph = # obtained by calling tf.Graph.as_graph_def()
with open("my_graph.bin", "wb") as f:
f.write(proto_graph.SerializeToString())
If you just want to write the file and do not care about the encoding you can also use tf.train.write_graph()
v = tf.Variable(0, name='my_variable')
sess = tf.Session()
tf.train.write_graph(sess.graph_def, '/tmp/my-model', 'train.pbtxt')
Note: Tested on TF 0.10, not sure about earlier versions.