Now I'm in front of the problem about inception v-3 and checkpoint data.
I have been tackling with updating inception-v3's checkpoint data by my images, reading the git page below and succeeded to make new checkpoint data.
https://github.com/tensorflow/models/tree/master/inception
I thought at first just by little change of the code, I can use those checkpoint data to recognise new image datas like the below url.
https://www.tensorflow.org/versions/master/tutorials/image_recognition/index.html
I thought at first that "classify.py" or something reads the new check point datas and just by "python classify.py -image something.png", the program recognises the image data. But It doesn't....
I really need a help.
thanks.
To have input .pb file, during training, import also tf.train.write_graph(sess.graph.as_graph_def(), 'path_to_folder', 'input_graph.pb',False)
If you have downloaded the inception v3 source code, in the inception_train.py, add the line I wrote above, under
saver.save(sess, checkpoint_path, global_step=step). (Where you save the checkpoint/s)
Hope this helps!
To use your checkpoints and model in something like the label_image example, you'll need to run the tensorflow/python/tools/freeze_graph script to convert your variables into constants stored inside the GraphDef. That's how we created the graph file used in that sample code, for example.
Related
I want to convert rcmalli_vggface_tf_vgg16.h5 pre-trained model to js format to use in a tensorflow-js project.
to convert this file I tried different ways but yet I could not to solve the problem.
I'm using converters.convert_tf_saved_model method to load and then convert it to a json file.
converters.convert_tf_saved_model('rcmalli_vggface_tf_vgg16.h5','web_model')
But every time following error is shown:
SavedModel file does not exist at: rcmalli_vggface_tf_vgg16.h5
While I am sure the h5 file is next to the file that is running the program.
I try full path address but same error occured. I do not know what is problem
You are converting a keras saved_model but the model is a keras layers_model because it is stored in h5 file.
I find this way the simplest to convert the model. And it did it in about 2 seconds.
Go to TensorflowJS converter Github
Follow installation instructions
Download the model you point to from the Github repo.
The model is written in the HDF5 file format
By the way, this is the oldest of the models, why not to download a new one ?
Also, this is a huge model, unless runs on the server it is of no use for a browser (produces 50 shard files of 4MB.)
Perform the conversion*
tensorflowjs_converter --input_format=keras rcmalli_vggface_tf_vgg16.h5 ./converted
The output will then be a layers model, and this has to be loaded with the tf.loadLayers API.
For example, use const model = await tf.loadGraphModel('path/to/model.json');
Note*: ./converted is the output directory, be sure not to overwrite your own stuff.
im quite new to object detection but i managed to train my first Tensorflow custom model yesterday. I think it worked fine besides some warnings, at least i got my exported_model folder with checkpoint, saved model and pipeline.config. I built it with exporter_main_v2.py from Tensorflow. I just loaded some images of deers and want to try to detect some on different pictures.
That's what i would like to test now, but i dont know how. I already did an object detection tutorial with pre trained models and it worked fine. I tried to just replace config_file_path, saved_model_path and image_path with the paths linking to my exported model but it didnt work:
error: OpenCV(4.6.0) D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\tensorflow\tf_io.cpp:42: error: (-2:Unspecified error) FAILED: ReadProtoFromBinaryFile(param_file, param). Failed to parse GraphDef file: D:\VSCode\Machine_Learning_Tests\Tensorflow\workspace\exported_models\first_model\saved_model\saved_model.pb in function 'cv::dnn::ReadTFNetParamsFromBinaryFileOrDie'
There are endless tutorials on how to train custom detection but i cant find a good explanation how to manually test my exported model.
Thanks in advance!
EDIT: I need to know how to build a script where i can import a model i saved with Tensorflow exporter_main_v2.py and an image i want to test this model on and get a result, either in text or with rectangels in picture. Seeing many tutorials but none works for me with a model i saved with Tensorflow exporter_main_v2.py
From the error it looks like you have a model saved as .pb. If you want to do inference you can write something like this:
# load the model
model = tf.keras.models.load_model(my_model_dir)
prediction = model.predict(x=x_test, ...)
You'll have to set x which is the only mandatory argument. It is your test dataset (the images you want to obtain predictions from). Also, predict is useful when you have a great amount of images to predict. It handles the prediction in a batched way, avoiding filling up the memory. If you have just a few you can use directly the __call__() method of your model, like this:
prediction = model(x_test, training=False)
More about prediction can be found at the Tensorflow documentation.
I used the transfer learning approach to develop a detection model using the faster_rcnn algorithm.
To evaluate my model, I used the following commands-
!python model_main_tf2.py --model_dir=models/faster_rcnn_inception_resnet_v2 --pipeline_config_path=models/faster_rcnn_inception_resnet_v2/pipeline.config --checkpoint_dir=models/faster_rcnn_inception_resnet_v2
However, I have been getting the following error/info message: -
INFO:tensorflow:Waiting for new checkpoint at models/faster_rcnn_inception_resnet_v2
I0331 23:23:11.699681 140426971481984 checkpoint_utils.py:139] Waiting for new checkpoint at models/faster_rcnn_inception_resnet_v2
I checked the path to the checkpoint_dir is correct. What could be the problem and how can I resolve it?
Thanks in advance.
You need to run another script for training to generate new checkpoint. model_main_tf2.py does not do both at once, i.e., it won't train model and evaluate the model at the end of each epoch.
One way to get what you want modifying checkpoint_max_to_keep in https://github.com/tensorflow/models/blob/13ec3c1460b928301d208115aed0c94fb47538b7/research/object_detection/model_lib_v2.py#L445
to keep all checkpoints, then evaluate separately. This does not work exactly same as you want, but it generates the curves.
A similar situation happened to me. I don't know if this is the solution or just a workaround but it did work for me. I simply exported my model and provided the path to that checkpoint folder.
fintune_checkpoint_model_directory
|
\---checkpoint(folder)
|
\---checkpoint(file with no extension)
\---ckpt-1.data0000of0001
\---ckpt-1.index
and then simply run the model_main_tf.py file for evaluation.
if you trained your model with a few number of steps it can be a problem maybe a few checkpoints can affect that TensorFlow can't generate evaluation so try to increase the number of steps
I fine-tuned im2txt model and obtained the ckpt.data, ckpt.index and ckpt.meta files and a graph.pbtxt file using the procedure in the im2txt github.
The model seems to work well as it produces almost correct captions.
Now I would like to freeze this model to use it on android.
I used the freeze_graph.py script in https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py.
python freeze_graph.py --input_graph=/path/to/graph.pbtxt --input_binary=false --input_checkpoint=/path/to/model.ckpt --output_graph=/path/to/output_graph.pb --output_node_names="softmax,lstm/initial_state,lstm/state"
And I have the following error : AssertionError: softmax is not in graph.
The discussion in https://github.com/tensorflow/models/issues/816 is about the same problem but it did not help me very much.
Indeed, when I look in the graph.pbtxt generated after fine-tuning, I cannot find softmax, lstm/initial_state and lstm/state.
But in the show_and_tell_model.py file of im2txt, the names of the tensors seem to be "softmax", "lstm/initial_state" and "lstm/state". So, I don't know what's happening.
I hope I was clear enough about what I've tried so far. Thanks in advance for any help.
Regards,
Stephane
Found and verified the answer: in inference_wrapper.base.py, just add something like saver.save(sess, "model/ckpt4") after saver.restore(sess, checkpoint_path) in def _restore_fn(sess):. Then rebuild and run_inference and you'll get a model that can be frozen, transformed, and optionally memmapped, to be loaded by iOS and Android apps.
For detailed commands to freeze, transform, and convert to memmapped, see my answer at Error using Model after using optimize_for_inference.py on frozen graph.
Ok,
I think I finally got the solution. In case it is useful for others, here it is:
After training, you obtain ckpt.data, ckpt.index and ckpt.meta files and a graph.pbtxt file.
You then have to load this model in 'inference' mode (see InferenceWrapper in im2txt). It builds a graph with the correct names 'softmax', 'lstm/initial_state' and 'lstm/state'. You save this graph (with the same ckpt format) and then you can apply the freeze_graph script to obtain the frozen model.
Regards,
Stephane
In the TensorFlow tutorial to train a network on CIFAR-10, where and how do they save the weights/parameters between running training and evaluation? I cannot see any files saved to my project directory.
Here are the links to the tutorial and the code:
https://www.tensorflow.org/versions/r0.11/tutorials/deep_cnn/index.html
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/models/image/cifar10
It saves the logs and checkpoints to the /tmp/ folder by default.
The weights are included in the checkpoint files.
As you can see in both eval and train files, it does take a checkpoint dir as parameter.
cifar10_train.py:
tf.app.flags.DEFINE_string('train_dir', '/tmp/cifar10_train',
"""Directory where to write event logs """
"""and checkpoint.""")
cifar10_eval.py:
tf.app.flags.DEFINE_string('eval_dir', '/tmp/cifar10_eval',
"""Directory where to write event logs.""")
tf.app.flags.DEFINE_string('eval_data', 'test',
"""Either 'test' or 'train_eval'.""")
tf.app.flags.DEFINE_string('checkpoint_dir', '/tmp/cifar10_train',
"""Directory where to read model checkpoints.""")
You can call those scripts with custom values for those. For my project using Inception I have to change it since the main hard drive does not have enough space for the bottlenecks created by inception.
It might be a good practice to explicitly set those values since the /tmp/ folder is not persistent and thus you might lose your training data.
The following code will save the training data into a custom folder.
python cifar10_train.py --train_dir="/home/username/train_folder"
and then, to evaluate:
python cifar10_eval.py --checkpoint_dir="/home/username/train_folder"
It also applies to the other examples.
Let's assume you're running cifar10_train, saving happens on this line:
https://github.com/tensorflow/tensorflow/blob/r0.11/tensorflow/models/image/cifar10/cifar10_train.py#L122
And the default location is defined in this line (it's "/tmp/cifar10_train"):
https://github.com/tensorflow/tensorflow/blob/r0.11/tensorflow/models/image/cifar10/cifar10_train.py#L51
In cifar10_eval, restoring the weights happens on this line:
https://github.com/tensorflow/tensorflow/blob/r0.11/tensorflow/models/image/cifar10/cifar10_eval.py#L75