I am using tensorflow-gpu==1.10.0 and keras from tensorflow as tf.keras.
I am trying to use source code written by someone else to implement it on my network.
I saved my network using save_model and load it using load_model. when I use model.get_config(), I expect a dictionary, but i"m getting a list. Keras source documentation also says that get_config returns a dictionary (https://keras.io/models/about-keras-models/).
I tried to check if it has to do with saving type : save_model or model.save that makes the difference in how it is saved, but both give me this error:
TypeError: list indices must be integers or slices, not str
my code block :
model_config = self.keras_model.get_config()
for layer in model_config['layers']:
name = layer['name']
if name in update_layers:
layer['config']['filters'] = update_layers[name]['filters']
my pip freeze :
absl-py==0.6.1
astor==0.7.1
bitstring==3.1.5
coverage==4.5.1
cycler==0.10.0
decorator==4.3.0
Django==2.1.3
easydict==1.7
enum34==1.1.6
futures==3.1.1
gast==0.2.0
geopy==1.11.0
grpcio==1.16.1
h5py==2.7.1
image==1.5.15
ImageHash==3.7
imageio==2.5.0
imgaug==0.2.5
Keras==2.1.3
kiwisolver==1.1.0
lxml==4.1.1
Markdown==3.0.1
matplotlib==2.1.0
networkx==2.2
nose==1.3.7
numpy==1.14.1
olefile==0.46
opencv-python==3.3.0.10
pandas==0.20.3
Pillow==4.2.1
prometheus-client==0.4.2
protobuf==3.6.1
pyparsing==2.3.0
pyquaternion==0.9.2
python-dateutil==2.7.5
pytz==2018.7
PyWavelets==1.0.1
PyYAML==3.12
Rtree==0.8.3
scikit-image==0.13.1
scikit-learn==0.19.1
scipy==0.19.1
Shapely==1.6.4.post1
six==1.11.0
sk-video==1.1.8
sklearn-porter==0.6.2
tensorboard==1.10.0
tensorflow-gpu==1.10.0
termcolor==1.1.0
tqdm==4.19.4
utm==0.4.2
vtk==8.1.0
Werkzeug==0.14.1
xlrd==1.1.0
xmltodict==0.11.0
Related
I am calling a model in a function detect:
def detect(img):
detector_output = detector(tf.reshape(img, (1, img.shape[0], img.shape[1], img.shape[2])))
classes = detector_output['detection_classes'][0].numpy()
most_likely = tf.convert_to_tensor(classes[0])
box = detector_output['detection_boxes'][0][0]
box = tf.math.multiply(box, [img.shape[0], img.shape[1], img.shape[0], img.shape[1]])
box = tf.cast(box, tf.int16)
return (box, most_likely)
this is called in another function reads via tf.data.datasets map api
dataset = dataset.map(reads, num_parallel_calls = AUTO).batch(32)
I think the issue is that this tensorflow hub model (or all object detection models I could find) does not support batching.
Calling the function via reads by itself works fine.
except if I use the tf.function decorator, then weirdly even by itself detect(img) throws the same error.
I tried with several models from here with the same result.
detector needs the shape with the 1 dimension up front.
I know there should be some reverse flatten() or squeeze() but I couldn't find it, apologies for the bad style!
The issue is also likely here in the reshaping.
the full error:
TypeError: Failed to convert object of type <class 'tuple'> to Tensor. Contents: (1, None, None, 3). Consider casting elements to a supported type.
Edit: I fixed the error by using tf.expand_dims instead of reshaping above.
I'd still be glad for a good explanation to understand better what went trong.
Thank you for your help!
I've been trying to use a hyperspectral image dataset that was in .mat files. I found that using the scipy library with its loadmat function I can load the hyperspectral images and selecting some bands to see them as an RGB.
def RGBread(image):
images = loadmat(image).get('new_image')
return abs(images[:,:,(12,6,4)])
def SIread(image):
images = loadmat(image).get('new_image')
return abs(images[:,:,:])
After trying to implement the pix2pix architecture I found an unexpected error. When passing the list of the names of the dataset files by a function that is responsible for load the data(which are still .mat files), Tensor Flow does not have a direct method for this reading or coding, so I get these data with my RGBread and SIread method and then I turned them into tensors.
def load_image(filename, augment=True):
inimg = tf.cast( tf.convert_to_tensor(RGBread(ImagePATH+'/'+filename)
,dtype=tf.float32),tf.float32)[...,:3]
tgimg = tf.cast( tf.convert_to_tensor(SIread(ImagePATH+'/'+filename)
,dtype=tf.float32),tf.float32)[...,:12]
inimg, tgimg = resize(inimg, tgimg,IMG_HEIGH,IMG_WIDTH)
if augment:
inimg, tgimg = random_jitter(inimg, tgimg)
return inimg, tgimg
When loading an image with the load_image method, using the name and path of a single .mat file (a hyperspectral image) of my dataset as argument of my function the method worked perfectly.
plt.imshow(load_train_image(tr_urls[1])[0])
The problem started when I created my dataSet tensor, because my RGBread function does not receive a tensor as a parameter since loadmat('.mat') expects a string. Having the following error.
train_dataset = tf.data.Dataset.from_tensor_slices(tr_urls)
train_dataset = train_dataset.map(load_train_image,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
TypeError: expected str, bytes or os.PathLike object, not Tensor
After reading a lot about reading .mat files I found a user who recommended passing the data to TFrecord format. I've been trying to do it but I couldn't. Someone could help me?
Rasterio may be useful here.
https://rasterio.readthedocs.io/en/latest/
It can read hyperspectral .tif which can be passed to tf.data using a tf.keras data-generator. It may be a bit slow and perhaps should be done before training rather than at runtime.
An alternative is to ask whether you need the geotiff metadata. If not, you can preprocess and save as numpy arrays for tfrecords.
I am currently trying to get a trained TF seq2seq model working with Tensorflow.js. I need to get the json files for this. My input is a few sentences and the output is "embeddings". This model is working when I read in the checkpoint however I can't get it converted for tf.js. Part of the process for conversion is to get my latest checkpoint frozen as a protobuf (pb) file and then convert that to the json formats expected by tensorflow.js.
The above is my understanding and being that I haven't done this before, it may be wrong so please feel free to correct if I'm wrong in what I have deduced from reading.
When I try to convert to the tensorflow.js format I use the following command:
sudo tensorflowjs_converter --input_format=tf_frozen_model
--output_node_names='embeddings'
--saved_model_tags=serve
./saved_model/model.pb /web_model
This then displays the error listed in this post:
ValueError: Input 0 of node Variable/Assign was passed int32 from
Variable:0 incompatible with expected int32_ref.
One of the problems I'm running into is that I'm really not even sure how to troubleshoot this. So I was hoping that perhaps one of you maybe had some guidance or maybe you know what my issue may be.
I have upped the code I used to convert the checkpoint file to protobuf at the link below. I then added to the bottom of the notebook an import of that file that is then providing the same error I get when trying to convert to tensorflowjs format. (Just scroll to the bottom of the notebook)
https://github.com/xtr33me/textsumToTfjs/blob/master/convert_ckpt_to_pb.ipynb
Any help would be greatly appreciated!
Still unsure as to why I was getting the above error, however in the end I was able to resolve this issue by just switching over to using TF's SavedModel via tf.saved_model. A rough example of what worked for me can be found below should anyone in the future run into something similar. After saving out the below model, I was then able to perform the tensorflowjs_convert call on it and export the correct files.
if first_iter == True: #first time through
first_iter = False
#Lets try saving this badboy
cwd = os.getcwd()
path = os.path.join(cwd, 'simple')
shutil.rmtree(path, ignore_errors=True)
inputs_dict = {
"batch_decoder_input": tf.convert_to_tensor(batch_decoder_input)
}
outputs_dict = {
"batch_decoder_output": tf.convert_to_tensor(batch_decoder_output)
}
tf.saved_model.simple_save(
sess, path, inputs_dict, outputs_dict
)
print('Model Saved')
#End save model code
Suppose now I have a training input pipeline which finally generate train_x and train_y using tf.train.shuffle_batch. I export meta graph and re-import the graph in another code file. Now I want to detach the input pipeline, i.e., the train_x and train_y, and connect a new test_x and test_y. How can I make accomplish this using tf.contrib.graph_editor?
EDIT: As suggested by #iga, I change my input directory using input_map
filenames = tf.train.match_filenames_once(FLAGS.data_dir + '*', name='matching_filenames')
if FLAGS.ckpt != '':
latest = FLAGS.log_dir + FLAGS.ckpt
else:
latest = tf.train.latest_checkpoint(FLAGS.log_dir)
if not latest or not os.path.exists(latest+'.meta'):
print("checkpoint " + latest + " does not exist")
sys.exit(1)
saver = tf.train.import_meta_graph(latest+'.meta',
input_map={'matching_filenames:0':filenames},
import_scope='import')
g = tf.get_default_graph()
but I get the following error:
ValueError: graph_def is invalid at node u'matching_filenames/Assign':
Input tensor 'matching_filenames:0' Cannot convert a tensor of type
string to an input of type string_ref.
Are there any elegant way to resolve this?
For this task, you should be able to just use input_map argument to https://www.tensorflow.org/api_docs/python/tf/import_graph_def. If you are using import_meta_graph, you can pass the input_map into its kwargs and it will get passed down to import_graph_def.
RESPONSE TO EDIT: I am assuming that your original graph (the one you are deserializing) had the same matching_filenames variable. Quite confusingly, the tensor name "matching_filenames:0" actually refers to the tensor going from the VariableV2 op to the Assign op. The type of this edge is string_ref and you don't really want to break that edge.
The output from a variable typically goes through an identity op called matching_filenames/read. This is what you want to use as the key in your input_map. For the value, you want the same tensor in your new filenames. So, your call should probably look like:
tf.train.import_meta_graph(latest+'.meta',
input_map={'matching_filenames/read': filenames.read_value()},
import_scope='import')
In general, variables are fairly complicated. If this does not work, you can use some placeholder op and feed the names into it manually.
How to save tensorflow specific variable to local disk as a ndarray?(not layer parameter,just a variable, or tensor)
Like :
ux=tf.Variable([10,1600,1,2])
tf.save('ux.npy',ux)
Is there anything like the code above? And after that I can load the ux.npy like:
ux = numpy.load('ux.npy')
Numpy has save and load function itself so for saving the numpy array you can use its function. The syntax is:
numpy.savetxt(Out_file_name, numpy_to_save)