I have some code which creates a graph to process some images and then iterates sess.run() in a loop to fetch batches of image tensors of shape [*, 299, 299, 3]. I'd like to then feed these images into the inception model.
So, I added some code to load the inception model:
def create_graph():
""""Creates a graph from saved GraphDef file and returns a saver."""
# Creates graph from saved graph_def.pb.
print 'Loading graph...'
with tf.Session() as sess:
with gfile.FastGFile('/web/tensorflow_transfer/resources/classify_image_graph_def.pb', 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
return sess.graph
g = create_graph()
for i in range(training_steps):
sess.run(...)
Now Im getting this error when running run():
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 368, in run
results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 420, in _do_run
raise RuntimeError(compat.as_text(tf_session.TF_Message(status)))
RuntimeError: Incompatible GraphDef versions in Extend: 1 != 0
This is most likely from using too old a version of TensorFlow to read in and run the graph -- the graph was created using a newer version of the GraphDef. Try upgrading to 0.7 or to HEAD and then run your code again.
Related
This is the error message i get-
Traceback:
File "/app/.heroku/python/lib/python3.9/site-packages/streamlit/script_runner.py", line 354, in _run_script
exec(code, module.__dict__)
File "/app/app.py", line 131, in <module>
label, perc = classifier(image, model_file)
File "/app/.heroku/python/lib/python3.9/site-packages/streamlit/legacy_caching/caching.py", line 574, in wrapped_func
return get_or_create_cached_value()
File "/app/.heroku/python/lib/python3.9/site-packages/streamlit/legacy_caching/caching.py", line 558, in get_or_create_cached_value
return_value = func(*args, **kwargs)
File "/app/app.py", line 52, in classifier
model = tf.saved_model.load("my_model")
File "/app/.heroku/python/lib/python3.9/site-packages/tensorflow/python/saved_model/load.py", line 900, in load
result = load_internal(export_dir, tags, options)["root"]
File "/app/.heroku/python/lib/python3.9/site-packages/tensorflow/python/saved_model/load.py", line 913, in load_internal
loader_impl.parse_saved_model_with_debug_info(export_dir))
File "/app/.heroku/python/lib/python3.9/site-packages/tensorflow/python/saved_model/loader_impl.py", line 60, in parse_saved_model_with_debug_info
saved_model = _parse_saved_model(export_dir)
File "/app/.heroku/python/lib/python3.9/site-packages/tensorflow/python/saved_model/loader_impl.py", line 108, in parse_saved_model
raise IOError(f"Cannot parse file {path_to_pb}: {str(e)}.")
I am trying to run a saved model (pb format) on Heroku. I am calling for loading a saved model using the snippet below-
model = tf.keras.models.load_model("./my_model")
# Create the array of the right shape to feed into the keras model
data = np.ndarray(shape=(1, 200, 200, 3), dtype=np.float32)
image = img
# image sizing
size = (200, 200)
image = ImageOps.fit(image, size, Image.ANTIALIAS)
# turn the image into a numpy array
image_array = np.asarray(image)
# Normalize the image
normalized_image_array = image_array.astype(np.float32) / 255
# Load the image into the array
data[0] = normalized_image_array
# run the inference
predict_dataset = tf.convert_to_tensor(np.array(normalized_image_array))
# training=False is needed only if there are layers with different
# behavior during training versus inference (e.g. Dropout).
predictions = model(predict_dataset, training=False)
prediction_percentage = predictions.numpy()[0][0]
prediction = prediction_percentage.round()
Please note that the model is a tensorflow model saved in pb format and contains multiple files such as variables.data 00000 out of 00001 and saved_model.pb
Edit- I would like to point out that the model was saved on tensorflow version 2.7.0 using the save method
#staticmethod
def model_saver(model_obj):
try:
model_obj.save("./FreshPrice/Output/my_model")
except:
print("Model not saved successfully!")
I am trying to run the model on heroku where I am using tensorflow-cpu (2.7.0) to run the loaded model. I used tf-cpu since it has lower size than tf.
Please advise on the error! any help is appreciated
Hi I was making tflite for custom albert model with pb file in tf1.15 but raised error of
raise IOError("Cannot parse file %s: %s." % (path_to_pb, str(e)))
OSError: Cannot parse file b'/home/choss/test2/freeze2/saved_model.pb': Error parsing message.
Code below is How I made .pb file
meta_path = 'model.ckpt-400.meta' # Your .meta file
output_node_names = ['loss/Softmax']
with tf.Session() as sess:
# Restore the graph
saver = tf.train.import_meta_graph(meta_path)
# Load weights
ckpt ='/home/choss/test2/freeze2/model.ckpt-400'
print(ckpt)
saver.restore(sess, ckpt)
output_node_names = [n.name for n in tf.get_default_graph().as_graph_def().node]
# Freeze the graph
frozen_graph_def = tf.graph_util.convert_variables_to_constants(
sess,
sess.graph_def,
output_node_names)
# Save the frozen graph
with open('saved_model.pb', 'wb') as f:
f.write(frozen_graph_def.SerializeToString())
And I tried to make tflite file with code below
saved_model_dir = "/home/choss/test2/freeze2"
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)
I used f.graph_util.convert_variables_to_constants because of freeze_graph because
freeze_graph.freeze_graph('./graph.pbtxt', saver, False, 'model.ckpt-400', 'loss/ArgMax', "", "", 'frozen.pb', True, "")
gave me an error message
File "/home/pgb/anaconda3/envs/myenv/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py", line 2154, in __getitem__
return self._inputs[i]
IndexError: list index out of range
Is it because I did not use freeze_graph?
If so is there any other way aside from freeze_graph?
Instead of freezing the graph by yourself, I recommend exporting as a TF saved model and using the saved model converter with the recent TF version. You can decouple the TensorFlow versions for training and converting. For example, training can be done in the TF 1.15 and the saved model can be exported from it. And then, it is possible to bring the saved model to the TFLite converter API in TensorFlow 2.4.1 version or beyonds.
Beforehand, I thank you for analyzing my post and helping out. I've recently gotten interested in ML with Tensorflow,
but I've encountered a problem with my code. I'm reading a book called Learning TensorFlow, and I've written out the whole thing
from the first example. They are analyzing MNIST images, and I've also added my own comments with my perspective on how things work
in the code. When I run the code, however, I get an error. Here's my code, and the error.
#Import tensorflow under the name of ts
import tensorflow as tf
#Import MNIST tutorial data from tensorflow
from tensorflow.examples.tutorials.mnist import input_data
#Declare constants
#Data path
DATA_DIR = 'C:/tmp/data'
#Number of steps
NUM_STEPS = 1000
#Number of examples per step
MINIBATCH_SIZE = 100
#When we read the data-set it saves it locally under our data path, or under c:/tmp/data
data = input_data.read_data_sets(DATA_DIR, one_hot = True)
#Our placeholder X is the image. Placeholders are supplied when running the computation graph
x = tf.placeholder(tf.float32, [None, 784])
#Create a variable representing the weights. Variables are manipulated by the computation graph
W = tf.Variable(tf.zeros([784, 10]))
y_true = tf.placeholder(tf.float32, [None, 784])
y_pred = tf.matmul(x, W)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits=y_pred, labels=y_true))
gd_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
correct_mask = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y_true, 1))
accuracy = tf.reduce_mean(tf.cast(correct_mask, tf.float32))
with tf.Session() as sess:
#Initialize global variables
sess.run(tf.global_variables_initializer())
for _ in range(NUM_STEPS):
batch_xs, batch_ys = data.train.next_batch(MINIBATCH_SIZE)
sess.run(gd_step, feed_dict={x: batch_xs, y_true: batch_ys})
ans = sess.run(accuracy, feed_dict={x: data.test.images,
y_true: data.test.labels})
print("Accuracy: {:.4}%".format(ans*100))
Now here's the error.
runfile('C:/Users/user/.spyder-py3/temp.py', wdir='C:/Users/user/.spyder-py3')
Extracting C:/tmp/data\train-images-idx3-ubyte.gz
Extracting C:/tmp/data\train-labels-idx1-ubyte.gz
Extracting C:/tmp/data\t10k-images-idx3-ubyte.gz
Extracting C:/tmp/data\t10k-labels-idx1-ubyte.gz
Traceback (most recent call last):
File "<ipython-input-11-bf503334b166>", line 1, in <module>
runfile('C:/Users/user/.spyder-py3/temp.py', wdir='C:/Users/CwWJc/.spyder-py3')
File "C:\Users\user\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 827, in runfile
execfile(filename, namespace)
File "C:\Users\user\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/user/.spyder-py3/temp.py", line 38, in <module>
sess.run(gd_step, feed_dict={x: batch_xs, y_true: batch_ys})
File "C:\Users\user\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 950, in run
run_metadata_ptr)
File "C:\Users\user\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1149, in _run
str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (100, 10) for Tensor
'Placeholder_15:0', which has shape '(?, 784)'
Any help is greatly appreciated. Sorry if I'm making a stupid mistake. I find that I often do, though. Thanks in advance! Also, sorry for garbage formatting. :)
Hahaha! I got y_true mixed up. Sorry for the hassle everyone.
I've successfully trained and saved a faster RCNN model for tensorflow using their object detection API. I'm now trying to run some inferences on the code, taking bits of code from this tutorial.
However, after I successfully restore the metagraph and the checkpoint, the system can't find the input and output nodes, I get the following error:
KeyError: "The name 'image_tensor:0' refers to a Tensor which does not
exist. The operation, 'image_tensor', does not exist in the graph."
The checkpoint and metagraph were created by the train.py script, on my own data, following the instructions given here.
This is my code:
OUTPUT_DIR = "my_path/models/SSD_v1/train"
CKPT_DIR = OUTPUT_DIR
LATEST_CKPT_FILENAME = "checkpoint"
LAST_CKPT_FILE = os.path.join(CKPT_DIR, LATEST_CKPT_FILENAME)
MODEL_FILENAME_PATH = os.path.join(OUTPUT_DIR, "model.ckpt.meta")
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
def test_model(images_list, path_to_ckpt=None,
meta_graph=None):
if path_to_ckpt is None:
path_to_ckpt = tf.train.latest_checkpoint(CKPT_DIR, LATEST_CKPT_FILENAME)
if meta_graph is None:
meta_graph = MODEL_FILENAME_PATH
print("test_model launched")
tf.reset_default_graph()
detection_graph = tf.Graph()
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
# Restore graph
saver = tf.train.import_meta_graph(meta_graph, clear_devices=True)
print('metagraph restored')
saver.restore(sess, path_to_ckpt)
print('graph restored')
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') # This is where the error happens
# Each box represents a part of the image where a particular object was detected.
detected_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
detected_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detected_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = graph.get_tensor_by_name('num_detections:0')
print("Output tensors: ")
print(detected_boxes)
print(detected_scores)
print(detected_classes)
print('')
for i, image in enumerate(images_list):
detected_boxes, detected_scores, detected_classes, num_detect = sess.run([detected_boxes, detected_scores, detected_classes, num_detections],
feed_dict={image_tensor: image})
print(i, num_detect, detected_boxes, detected_scores, detected_classes)
def main():
directory_path = "../data/samples/"
image_files = [f for f in os.listdir(directory_path) if os.path.isfile(os.path.join(directory_path, f))]
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_list = [ np.expand_dims(load_image_into_numpy_array(Image.open(os.path.join(directory_path, f))), axis=0) for f in image_files]
test_model(images_list=image_list)
if __name__=="__main__":
main()
Full error stacktrace:
Traceback (most recent call last): File "/home/guillaumedelaboulaye/PR8210PANO/faster-rcnn/pano_faster_rcnn/src/run_faster_rcnn_inference.py", line 99, in <module>
main() File "/home/guillaumedelaboulaye/PR8210PANO/faster-rcnn/pano_faster_rcnn/src/run_faster_rcnn_inference.py", line 95, in main
test_model(images_list=image_list) File "/home/guillaumedelaboulaye/PR8210PANO/faster-rcnn/pano_faster_rcnn/src/run_faster_rcnn_inference.py", line 48, in test_model
image_tensor = graph.get_tensor_by_name('image_tensor:0') File "/home/guillaumedelaboulaye/PR8210PANO/faster-rcnn/venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2733, in get_tensor_by_name
return self.as_graph_element(name, allow_tensor=True, allow_operation=False) File "/home/guillaumedelaboulaye/PR8210PANO/faster-rcnn/venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2584, in as_graph_element
return self._as_graph_element_locked(obj, allow_tensor, allow_operation) File "/home/guillaumedelaboulaye/PR8210PANO/faster-rcnn/venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2626, in _as_graph_element_locked
"graph." % (repr(name), repr(op_name))) KeyError: "The name 'image_tensor:0' refers to a Tensor which does not exist. The operation, 'image_tensor', does not exist in the graph."
In the train graph, the input/output nodes are not given those names. What you will need to do is to "export" your trained model via the export_inference_graph.py tool. I believe it currently exports it to a frozen graph or a SavedModel, but in future releases, it will export to ordinary checkpoint as well.
If you want sample code for finding the node names of the graph, referring to the object_detection_tutorial.ipynb, after the "Load a (frozen) Tensorflow model into memory." block:
for node in od_graph_def.node:
print node.name
That should list all the node names that you can then enter in the subsequent blocks.
I am trying to create an image classifier that utilizes the pre-trained ResNet V2 model provided in the slim documentation.
Here is the code so far:
import tensorflow as tf
slim = tf.contrib.slim
from PIL import Image
from inception_resnet_v2 import *
import numpy as np
checkpoint_file = 'inception_resnet_v2_2016_08_30.ckpt'
sample_images = ['carrot.jpg']
input_tensor = tf.placeholder(tf.float32, shape=(None,299,299,3), name='input_image')
scaled_input_tensor = tf.scalar_mul((1.0/255), input_tensor)
scaled_input_tensor = tf.subtract(scaled_input_tensor, 0.5)
scaled_input_tensor = tf.multiply(scaled_input_tensor, 2.0)
variables_to_restore = slim.get_model_variables()
print(variables_to_restore)
init_fn = slim.assign_from_checkpoint_fn(
checkpoint_file,
slim.get_model_variables('InceptionResnetV2'))
sess = tf.Session()
init_fn(sess)
arg_scope = inception_resnet_v2_arg_scope()
with slim.arg_scope(arg_scope):
logits, end_points = inception_resnet_v2(scaled_input_tensor, is_training=False)
for image in sample_images:
im = Image.open(image).resize((299,299))
im = np.array(im)
im = im.reshape(-1,299,299,3)
predict_values, logit_values = sess.run([end_points['Predictions'], logits], feed_dict={input_tensor: im})
print (np.max(predict_values), np.max(logit_values))
print (np.argmax(predict_values), np.argmax(logit_values))
The problem is I keep getting this error:
Traceback (most recent call last):
File "./classify.py", line 21, in <module>
slim.get_model_variables('InceptionResnetV2'))
File "/home/ubuntu/tensorflow/local/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/variables.py", line 584, in assign_from_checkpoint_fn
saver = tf_saver.Saver(var_list, reshape=reshape_variables)
File "/home/ubuntu/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1040, in __init__
self.build()
File "/home/ubuntu/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1061, in build
raise ValueError("No variables to save")
ValueError: No variables to save
So it seems TF/Slim is unable to find any variables and this is made clear when I call:
variables_to_restore = slim.get_model_variables()
print(variables_to_restore)
As it outputs an empty array.
How can I go about using the pre-trained model?
This happens because you haven't constructed the model in your graph yet to have any variables starting with the name "InceptionResnetV2" to be captured and restored by the saver.
I believe you should put the model construction before using slim.get_variables_to_restore().
For instance:
with slim.arg_scope(arg_scope):
logits, end_points = inception_resnet_v2(scaled_input_tensor, is_training=False)
variables_to_restore = slim.get_model_variables()
This way, the Tensor variables will be constructed and you should see variables_to_restore is no longer empty.
You need to manually add the model variables.
Try this
with slim.arg_scope(arg_scope):
logits, end_points = inception_resnet_v2(scaled_input_tensor, is_training=False)
# Add model variables
for var in tf.global_variables(scope='inception_resnet_v2'):
slim.add_model_variable(var)