I have trained a customer object detection using
python model_main_tf2.py \
--pipeline_config_path=/[ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu8/pipeline.config][1] \
--model_dir=/content/drive/MyDrive/training_object \
--alsologtostderr
I would like to prune the resulting model according to the official guide here. however, the guide works with keras format model and the result of model_main_tf2 is tensorflow.python.saved_model.load.Loader._recreate_base_user_object.<locals>._UserObject and does not have meta data
Related
I downloaded the vgg_19_2016_08_28.tar.gz and extracted a vgg-19.pb graph. I am using this for tf2onnx. However, this seems to have some dynamic parameters and hence tf2onnx if failing. I want to check if the vgg-19.pb is a frozen graph, if not how can I get a frozen vgg_19.pb graph?
Same question for tensorflow_inception_graph - inception_v3_2016_08_28.tar.gz
Same question for resnet - resnet_v1_50_2016_08_28.tar.gz
All downloaded from here - https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models
To convert TF models to ONNX you need to freeze the graph. The TensorFlow tool to freeze the graph is https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py
For example
python -m tensorflow.python.tools.freeze_graph \
--input_graph=my_checkpoint_dir/graphdef.pb \
--input_binary=true \
--output_node_names=output \
--input_checkpoint=my_checkpoint_dir \
--output_graph=tests/models/fc-layers/frozen.pb
To find the inputs and outputs for the TensorFlow graph the model developer will know or you can consult TensorFlow's summarize_graph tool (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms), for example:
summarize_graph --in_graph=tests/models/fc-layers/frozen.pb
I have been trying TFLite to increase detection speed on Android but strangely my .tflite model now almost only detects 1 category.
I have done testing on the .pb model that I got after retraining a mobilenet and the results are good but for some reason, when I convert it to .tflite the detection is way off...
For the retraining I used the retrain.py file from Tensorflow for poets 2
I am using the following commands to retrain, optimize for inference and convert the model to tflite:
python retrain.py \
--image_dir ~/tf_files/tw/ \
--tfhub_module https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/feature_vector/1 \
--output_graph ~/new_training_dir/retrainedGraph.pb \
-–saved_model_dir ~/new_training_dir/model/ \
--how_many_training_steps 500
sudo toco \
--input_file=retrainedGraph.pb \
--output_file=optimized_retrainedGraph.pb \
--input_format=TENSORFLOW_GRAPHDEF \
--output_format=TENSORFLOW_GRAPHDEF \
--input_shape=1,224,224,3 \
--input_array=Placeholder \
--output_array=final_result \
sudo toco \
--input_file=optimized_retrainedGraph.pb \
--input_format=TENSORFLOW_GRAPHDEF \
--output_format=TFLITE \
--output_file=retrainedGraph.tflite \
--inference_type=FLOAT \
--inference_input_type=FLOAT \
--input_arrays=Placeholder \
--output_array=final_result \
--input_shapes=1,224,224,3
Am I doing anything wrong here? Where could the loss in accuracy come from?
I faced the same issue while I was trying to convert a .pb model into .lite.
In fact, my accuracy would come down from 95 to 30!
Turns out the mistake I was committing was not during the conversion of .pb to .lite or in the command involved to do so. But it was actually while loading the image and pre-processing it before it is passed into the lite model and inferred using
interpreter.invoke()
command.
The below code you see is what I meant by pre-processing:
test_image=cv2.imread(file_name)
test_image=cv2.resize(test_image,(299,299),cv2.INTER_AREA)
test_image = np.expand_dims((test_image)/255, axis=0).astype(np.float32)
interpreter.set_tensor(input_tensor_index, test_image)
interpreter.invoke()
digit = np.argmax(output()[0])
#print(digit)
prediction=result[digit]
As you can see there are two crucial commands/pre-processing done on the image once it is read using "imread()":
i) The image should be resized to the size that is the "input_height" and "input_width" values of the input image/tensor that was used during the training. In my case (inception-v3) this was 299 for both "input_height" and "input_width". (Read the documentation of the model for this value or look for this variable in the file that you used to train or retrain the model)
ii) The next command in the above code is:
test_image = np.expand_dims((test_image)/255, axis=0).astype(np.float32)
I got this from the "formulae"/model code:
test_image = np.expand_dims((test_image-input_mean)/input_std, axis=0).astype(np.float32)
Reading the documentation revealed that for my architecture input_mean = 0 and input_std = 255.
When I did the said changes to my code, I got the accuracy that was expected (90%).
Hope this helps.
Please file an issue on GitHub https://github.com/tensorflow/tensorflow/issues and add the link here.
Also please add more details on what you are retraining the last layer for.
I am trying to convert a fine-tuned tensorflow inception_v3 model to uff format which can be run on NVIDIA's Jetson TX2. For conversion to uff, certain ops are supported, some are not. I am able to successfully freeze and convert to uff inception_v3 model with imagenet checkpoint provided by tensorflow. However if I fine-tune the model, additional ops like Floor, RandomUniform, etc are added in the new graph which are not yet supported. These layers remain even after freezing the model. This is happening in the fine-tuning for flowers sample provided on tensorflow site as well.
I want to understand why additional ops are added in the graph, while fine-tuning is just supposed to modify the final layer to match number of outputs required.
If they are added while training, how can I get rid of them? What post-processing steps tensorflow team followed before releasing inception_v3 model for imagenet?
I can share the pbtxt files if needed. For now, model layers details are uploaded at https://github.com/shrutim90/TF_to_UFF_Issue. I am using Tensorflow 1.6 with GPU.
I am following the steps to freeze or fine-tune the model from: https://github.com/tensorflow/models/tree/master/research/slim#Pretrained. As described in the above link, to reproduce the issue, install TF-Slim image models library and follow these steps:
1. python export_inference_graph.py \
--alsologtostderr \
--model_name=inception_v3 \
--output_file=/tmp/inception_v3_inf_graph.pb
2. python freeze_graph.py \
--input_graph=/tmp/inception_v3_inf_graph.pb \
--input_checkpoint=/tmp/checkpoints/inception_v3.ckpt \
--input_binary=true --output_graph=/tmp/frozen_inception_v3.pb \
--output_node_names=InceptionV3/Predictions/Reshape_1
3. DATASET_DIR=/tmp/flowers
TRAIN_DIR=/tmp/flowers-models/inception_v3
CHECKPOINT_PATH=/tmp/my_checkpoints/inception_v3.ckpt
python train_image_classifier.py --train_dir=$TRAIN_DIR --dataset_dir=$DATASET_DIR --dataset_name=flowers --dataset_split_name=train --model_name=inception_v3 --checkpoint_path=${CHECKPOINT_PATH} --checkpoint_exclude_scopes=InceptionV3/Logits,InceptionV3/AuxLogits --trainable_scopes=InceptionV3/Logits,InceptionV3/AuxLogits
4. python freeze_graph.py \
--input_graph=/tmp/graph.pbtxt \
--input_checkpoint=/tmp/checkpoints/model.ckpt-2539 \
--input_binary=false --output_graph=/tmp/frozen_inception_v3_flowers.pb \
--output_node_names=InceptionV3/Predictions/Reshape_1
To check the layers, you can check out .pbtxt file or use NVIDIA's convert-to-uff utility.
Run training script -> export_inference_graph -> freeze_graph . This gets rid of all the extra nodes and model can be easily converted to uff.
I see the guide of converting tensorflow pb model, only given to mobilenet model
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite#step-2-model-format-conversion
So my question is, can I convert the tensorflow inception pb model to tflite model?
If yes, where can I get the checkpoint (ckpt) file? I can't find them for inception model in https://github.com/tensorflow/models/tree/master/research/slim/nets.
Did I miss anything?
Yes, you should also be able to convert an inception model to TFLITE. You only need the checkpoints if the graph is not yet frozen. If the graph is already frozen (what I assume), you should be able to convert it with the following command:
bazel run --config=opt //tensorflow/contrib/lite/toco:toco -- \
--input_file=**/path/to/your/graph.pb** \
--output_file=**/path/to/your/output.tflite** \
--input_format=TENSORFLOW_GRAPHDEF \
--output_format=TFLITE \
--inference_type=FLOAT \
--input_shape=1,299,299,3 \
--input_array=**your_input** \
--output_array=**your_final_tensor**
(you have to replace the text between the asterisks with the arguments that applies to your case; --inputs=Mul for example)
Note on --inputs=Mul
Some of the TF commands used in Inception v3 are not supported by TFLITE (decodejpeg, expand_dims), since they typically do not have to be adopted by the model on the mobile phone (these tasks are done directly in the app code). Therefore you have to define where you want to hook into the graph with TF Lite.
You will probably get the following error message without using input_array:
Some of the operators in the model are not supported by the standard TensorFlow Lite runtime. If you have a custom implementation for them you can disable this error with --allow_custom_ops. Here is a list of operators for which you will need custom implementations: DecodeJpeg, ExpandDims.
I hope I could help you. I'm just struggling with converting retrained graphs around.
so I would like to use to TF Object Detection API to calculate mAP scores for YOLO.
Currently I'm training my YOLO model and it is producing ckpt files/frozen graph files.
I would like to take my YOLO model and evaluate it using the TF Obj Dec API. I know Eval.py says
2) Three configuration files may be provided: a model_pb2.DetectionModel
configuration file to define what type of DetectionModel is being evaluated, an
input_reader_pb2.InputReader file to specify what data the model is evaluating
and an eval_pb2.EvalConfig file to configure evaluation parameters.
Example usage:
./eval \
--logtostderr \
--checkpoint_dir=path/to/checkpoint_dir \
--eval_dir=path/to/eval_dir \
--eval_config_path=eval_config.pbtxt \
--model_config_path=model_config.pbtxt \
--input_config_path=eval_input_config.pbtxt
However, with darkflow there is no model_pb2.DetectonModel Configuration file. Is this possible?