I'm trying to load a pretrained retinanet model with keras by running:
# import keras
import keras
# import keras_retinanet
from keras_retinanet import models
from keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image
from keras_retinanet.utils.visualization import draw_box, draw_caption
from keras_retinanet.utils.colors import label_color
# set tf backend to allow memory to grow, instead of claiming everything
import tensorflow as tf
def get_session():
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
return tf.Session(config=config)
model_path = os.path.join('sample_data/snapshots', sorted(os.listdir('sample_data/snapshots'), reverse=True)[0])
print(model_path)
# load retinanet model
model = models.load_model(model_path, backbone_name='resnet50')
model = models.convert_model(model)
I am facing the following error with both codes:
OSError: SavedModel file does not exist at: sample_data/snapshots/training_5000(640_480).h5/{saved_model.pbtxt|saved_model.pb}
the cause might be some new versions of Keras or tensorflow,
soo I am going to list the versions that I am currently using.
keras.__version__
2.4.3
tf.__version__
2.4.1
Note: I am trying to run this code in my Colab.
Related
I am trying to build a NER model in Keras using ELMO Embeddings. SO I stumped across this tutorial and started implementing. I got lots of errors and some of them are as:
import tensorflow as tf
import tensorflow_hub as hub
from keras import backend as K
sess = tf.Session()
K.set_session(sess)
elmo_model = hub.Module("https://tfhub.dev/google/elmo/2", trainable=True)
sess.run(tf.global_variables_initializer())
sess.run(tf.tables_initializer())
def ElmoEmbedding(x):
return elmo_model(inputs={"tokens": tf.squeeze(tf.cast(x, tf.string)),
"sequence_len": tf.constant(batch_size*[max_len])},signature="tokens",as_dict=True)["elmo"]
input_text = Input(shape=(max_len,), dtype=tf.string)
embedding = Lambda(ElmoEmbedding, output_shape=(None, 1024))(input_text)
It gives me AttributeError: module 'tensorflow' has no attribute 'Session' . So if I comment out sess= code and run, it gives me AttributeError: module 'keras.backend' has no attribute 'set_session'.
Then again, Elmo code line is giving me RuntimeError: Exporting/importing meta graphs is not supported when eager execution is enabled. No graph exists when eager execution is enabled..
I have the following configurations:
tf.__version__
'2.3.1'
keras.__version__
'2.4.3'
import sys
sys.version
'3.8.3 (default, Jul 2 2020, 17:30:36) [MSC v.1916 64 bit (AMD64)]'
How can I use ELMO Embeddings in Keras Model?
You are using the old Tensorflow 1.x syntax but you have tensorflow 2 installed.
This is the new way to do elmo in TF2
Extracting ELMo features using tensorflow and convert them to numpy
The code is like this:
import tensorflow as tf
from keras_contrib.layers import CRF
from tensorflow import keras
def create_model(max_seq_len, adapter_size=64):
"""Creates a classification model."""
# adapter_size = 64 # see - arXiv:1902.00751
# create the bert layer
with tf.io.gfile.GFile(bert_config_file, "r") as reader:
bc = StockBertConfig.from_json_string(reader.read())
bert_params = map_stock_config_to_params(bc)
bert_params.adapter_size = adapter_size
bert = BertModelLayer.from_params(bert_params, name="bert")
input_ids = keras.layers.Input(shape=(max_seq_len,), dtype='int32', name="input_ids")
# token_type_ids = keras.layers.Input(shape=(max_seq_len,), dtype='int32', name="token_type_ids")
# output = bert([input_ids, token_type_ids])
bert_output = bert(input_ids)
print("bert_output.shape: {}".format(bert_output.shape)) # (?, 100, 768)
crf = CRF(len(tag2idx))
logits = crf(bert_output)
model = keras.Model(inputs=input_ids, outputs=logits)
model.build(input_shape=(None, max_seq_len))
# load the pre-trained model weights
load_stock_weights(bert, bert_ckpt_file)
# freeze weights if adapter-BERT is used
if adapter_size is not None:
freeze_bert_layers(bert)
model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
model.summary()
return model
I am using tensorflow keras and also use keras_contrib package, to do NER. it seems the tensorflow keras package does not work well with keras_contrib package.
The Traceback information is listed below:
Traceback (most recent call last):
File "F:/_gitclone3/bert_examples/bert_ner_example_eval.py", line 120, in <module>
model = create_model(max_seq_len, adapter_size=adapter_size)
File "F:/_gitclone3/bert_examples/bert_ner_example_eval.py", line 101, in create_model
logits = crf(bert_output)
File "C:\Users\yuexiang\Anaconda3\lib\site-packages\keras\engine\base_layer.py", line 443, in __call__
previous_mask = _collect_previous_mask(inputs)
File "C:\Users\yuexiang\Anaconda3\lib\site-packages\keras\engine\base_layer.py", line 1311, in _collect_previous_mask
mask = node.output_masks[tensor_index]
AttributeError: 'Node' object has no attribute 'output_masks'
How do I use CRF with tensorflow keras?
I run into a similar problem and spent a lot of time trying to get things to work. Here's what worked for me using python 3.6.5:
Seqeval:
pip install seqeval==0.0.5
Keras:
pip install keras==2.2.4
Keras-contrib (2.0.8):
git clone https://www.github.com/keras-team/keras-contrib.git
cd keras-contrib
python setup.py install
TensorFlow:
pip install tensorflow==1.14.0
Do pip list to make sure you have actually installed those versions (eg pip seqeval may automatically update your keras)
Then in your code import like so:
from keras.models import *
from keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional, Input
from keras_contrib.layers import CRF
#etc.
Hope this helps, good luck!
You can try tensorflow add-ons.(If you are using tensorflow version 2).
You can try tf-crf-layer (if you are using tensorflow==1.15.0)
They have it mentioned on their README.
git clone https://www.github.com/keras-team/keras-contrib.git
cd keras-contrib
python convert_to_tf_keras.py
USE_TF_KERAS=1 python setup.py install
I have gone through the possible solutions, mentioning which worked for me:
Install tf2crf (https://pypi.org/project/tf2crf/): It provides a simple CRF layer for TensorFlow 2 keras.
Use TensorFlow SIG Addons: ( https://www.tensorflow.org/addons/api_docs/python/tfa/layers/CRF): It provides the functionality that is not available in core TensorFlow.
I load a saved h5 model and want to save the model as pb.
The model is saved during training with the tf.keras.callbacks.ModelCheckpoint callback function.
TF version: 2.0.0a
edit: same issue also with 2.0.0-beta1
My steps to save a pb:
I first set K.set_learning_phase(0)
then I load the model with tf.keras.models.load_model
Then, I define the freeze_session() function.
(optional I compile the model)
Then using the freeze_session() function with tf.keras.backend.get_session
The error I get, with and without compiling:
AttributeError: module 'tensorflow.python.keras.api._v2.keras.backend'
has no attribute 'get_session'
My Question:
Does TF2 not have the get_session anymore?
(I know that tf.contrib.saved_model.save_keras_model does not exist anymore and I also tried tf.saved_model.save which not really worked)
Or does get_session only work when I actually train the model and just loading the h5 does not work
Edit: Also with a freshly trained session, no get_session is available.
If so, how would I go about to convert the h5 without training to pb? Is there a good tutorial?
Thank you for your help
update:
Since the official release of TF2.x graph/session concept has changed. The savedmodel api should be used.
You can use the tf.compat.v1.disable_eager_execution() with TF2.x and it will result in a pb file. However, I am not sure what kind of pb file type it is, as saved model composition changed from TF1 to TF2. I will keep digging.
I do save the model to pb from h5 model:
import logging
import tensorflow as tf
from tensorflow.compat.v1 import graph_util
from tensorflow.python.keras import backend as K
from tensorflow import keras
# necessary !!!
tf.compat.v1.disable_eager_execution()
h5_path = '/path/to/model.h5'
model = keras.models.load_model(h5_path)
model.summary()
# save pb
with K.get_session() as sess:
output_names = [out.op.name for out in model.outputs]
input_graph_def = sess.graph.as_graph_def()
for node in input_graph_def.node:
node.device = ""
graph = graph_util.remove_training_nodes(input_graph_def)
graph_frozen = graph_util.convert_variables_to_constants(sess, graph, output_names)
tf.io.write_graph(graph_frozen, '/path/to/pb/model.pb', as_text=False)
logging.info("save pb successfully！")
I use TF2 to convert model like:
pass keras.callbacks.ModelCheckpoint(save_weights_only=True) to model.fit and save checkpoint while training;
After training, self.model.load_weights(self.checkpoint_path) load checkpoint;
self.model.save(h5_path, overwrite=True, include_optimizer=False) save as h5;
convert h5 to pb just like above;
I'm wondering the same thing, as I'm trying to use get_session() and set_session() to free up GPU memory. These functions seem to be missing and aren't in the TF2.0 Keras documentation. I imagine it has something to do with Tensorflow's switch to eager execution, as direct session access is no longer required.
use
from tensorflow.compat.v1.keras.backend import get_session
in keras 2 & tensorflow 2.2
then call
import logging
import tensorflow as tf
from tensorflow.compat.v1 import graph_util
from tensorflow.python.keras import backend as K
from tensorflow import keras
from tensorflow.compat.v1.keras.backend import get_session
# necessary !!!
tf.compat.v1.disable_eager_execution()
h5_path = '/path/to/model.h5'
model = keras.models.load_model(h5_path)
model.summary()
# save pb
with get_session() as sess:
output_names = [out.op.name for out in model.outputs]
input_graph_def = sess.graph.as_graph_def()
for node in input_graph_def.node:
node.device = ""
graph = graph_util.remove_training_nodes(input_graph_def)
graph_frozen = graph_util.convert_variables_to_constants(sess, graph, output_names)
tf.io.write_graph(graph_frozen, '/path/to/pb/model.pb', as_text=False)
logging.info("save pb successfully！")
I'm trying to upload my saved model to ML engine so I can consume my model online, however I am getting the below error:
I am using tensorflow version 1.5 locally to train my model, based on the Tensorflow for poets tutorial (https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/).
I am then converting my model using the below 'save_model.py' script:
import tensorflow as tf
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.saved_model import builder as saved_model_builder
input_graph = 'retrained_graph.pb'
saved_model_dir = 'my_model'
with tf.Graph().as_default() as graph:
# 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('DecodeJpeg/contents:0')
inputs = {'image_bytes': tf.saved_model.utils.build_tensor_info(in_image)}
out_classes = graph.get_tensor_by_name('final_result: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()
The error message saying please use runtime 1.2 or above is talking about tensorflow? Or is my save_model.py doing something incorrectly?
You will need to use gcloud to deploy your model. The console does not let you manually specify the runtime version (i.e. it assumes TensorFlow 1.0). Further note that 1.5 is not yet available but will be very soon. That said, your model might work with 1.4, so it's worth a try.
The command to run is:
gcloud ml-engine versions create --model mymodel --origin=gs://mybucket --runtime-version 1.4
And in the near future you can use --runtime-version 1.5.
For more info, see the reference docs, particular the gcloud examples.
In tensorflow I can do something like this when creating a session:
tf.GPUOptions(per_process_gpu_memory_fraction=0.333,allow_growth=True)
Is there a way to do the same in keras with the tensorflow backend?
You can set the Keras global tensorflow session with keras.backend.tensorflow_backend.set_session():
import tensorflow as tf
import keras.backend.tensorflow_backend as ktf
def get_session(gpu_fraction=0.333):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction,
allow_growth=True)
return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
ktf.set_session(get_session())