I'm trying to use object_detection model with tf 1.4. I realize that there is no training directory that used to contain some ckpt files as in the previous tf versions, so any suggestions where is the new destination or should I use the ones from old versions?
I'm using the below command
python3 export_inference_graph.py \
--input_type image_tensor \
--pipeline_config_path samples/configs/faster_rcnn_resnet101_coco.config \
--trained_checkpoint_prefix model.ckpt \
--output_directory inference
You can download the full sets including the .ckpt files from this link
Related
I want to compile the TensorFlow Graph to Movidius Graph. I have used Model Zoo's ssd_mobilenet_v1_coco model to train it on my own dataset.
Then I ran
python object_detection/export_inference_graph.py \
--input_type=image_tensor \
--pipeline_config_path=/home/redtwo/nsir/ssd_mobilenet_v1_coco.config \
--trained_checkpoint_prefix=/home/redtwo/nsir/train/model.ckpt-3362 \
--output_directory=/home/redtwo/nsir/output
which generates me frozen_interference_graph.pb & saved_model/saved_model.pb
Now to convert this saved model into Movidius graph. There are commands given
Export GraphDef file
python3 ../tensorflow/tensorflow/python/tools/freeze_graph.py \
--input_graph=inception_v3.pb \
--input_binary=true \
--input_checkpoint=inception_v3.ckpt \
--output_graph=inception_v3_frozen.pb \
--output_node_name=InceptionV3/Predictions/Reshape_1
Freeze model for inference
python3 ../tensorflow/tensorflow/python/tools/freeze_graph.py \
--input_graph=inception_v3.pb \
--input_binary=true \
--input_checkpoint=inception_v3.ckpt \
--output_graph=inception_v3_frozen.pb \
--output_node_name=InceptionV3/Predictions/Reshape_1
which can finally be feed to NCS Intel Movidius SDK
mvNCCompile -s 12 inception_v3_frozen.pb -in=input -on=InceptionV3/Predictions/Reshape_1
All of this is given at Intel Movidius Website here: https://movidius.github.io/ncsdk/tf_modelzoo.html
My model was already trained i.e. output/frozen_inference_graph. Why do I again freeze it using /slim/export_inference_graph.py or it's the output/saved_model/saved_model.py that will go as input to slim/export_inference_graph.py??
All I want is output_node_name=Inceptionv3/Predictions/Reshape_1. How to get this output_name_name directory structure & anything inside it? I don't know what all it contains
what output node should I use for model zoo's ssd_mobilenet_v1_coco model(trained on my own custom dataset)
python freeze_graph.py \
--input_graph=/path/to/graph.pbtxt \
--input_checkpoint=/path/to/model.ckpt-22480 \
--input_binary=false \
--output_graph=/path/to/frozen_graph.pb \
--output_node_names="the nodes that you want to output e.g. InceptionV3/Predictions/Reshape_1 for Inception V3 "
Things I understand & don't understand:
input_checkpoint: ✓ [check points that were created during training]
output_graph: ✓ [path to output frozen graph]
out_node_names: X
I don't understand out_node_names parameter & what should inside this considering its ssd_mobilnet not inception_v3
System information
What is the top-level directory of the model you are using:
Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 16.04
TensorFlow installed from (source or binary): TensorFlow installed with pip
TensorFlow version (use command below): 1.13.1
Bazel version (if compiling from source):
CUDA/cuDNN version: V10.1.168/7.*
GPU model and memory: 2080Ti 11Gb
Exact command to reproduce:
The graph in saved_model/saved_model.pb is the graph definition(graph architecture) of the pretrained inception_v3 model without the weights loaded to the graph. The frozen_interference_graph.pb is the graph frozen with the checkpoints you have provided and taking the default output nodes of the inception_v3 model.
To get output node names summarise_graph tool can be used
You can use the below commands to use summarise_graph tool if bazel is installed
bazel build tensorflow/tools/graph_transforms:summarize_graph
bazel-bin/tensorflow/tools/graph_transforms/summarize_graph \
--in_graph=/tmp/inception_v3_inf_graph.pb
In case if bazel is not installed Output nodes can be obtained using the tensorboard or any other graph visualising tools like Netron.
The additional freeze_graph.py can be used to freeze the graph specifying the output nodes(ie in a case where additional output nodes are added to the inceptionV3). The frozen_interference_graph.pb is also an equaly good fit for infrencing.
I'm using MobileNetV2 and trying to get it working for Google Coral. Everything seems to work except the Coral Web Compiler, throws a random error, Uncaught application failure. So I think the problem is the intemidary steps required. For example, I'm using this with tflite_convert
tflite_convert \
--graph_def_file=optimized_graph.pb \
--output_format=TFLITE \
--output_file=mobilenet_v2_new.tflite \
--inference_type=FLOAT \
--inference_input_type=FLOAT \
--input_arrays=input \
--output_arrays=final_result \
--input_shapes=1,224,224,3
What am I getting wrong?
This is most likely because your model is not quantized. Edge TPU devices do not currently support float-based model inference. For the best results, you should enable quantization during training (described in the link). However, you can also apply quantization during TensorFlow Lite conversion.
With post-training quantization, you sacrifice accuracy but can test something out more quickly. When you convert your graph to TensorFlow Lite format, set inference_type to QUANTIZED_UINT8. You'll also need to apply the quantization parameters (mean/range/std_dev) on the command line as well.
tflite_convert \
--graph_def_file=optimized_graph.pb \
--output_format=TFLITE \
--output_file=mobilenet_v2_new.tflite \
--inference_type=QUANTIZED_UINT8 \
--input_arrays=input \
--output_arrays=final_result \
--input_shapes=1,224,224,3 \
--mean_values=128 --std_dev_values=127 \
--default_ranges_min=0 --default_ranges_max=255
You can then pass the quantized .tflite file to the model compiler.
For more details on the Edge TPU model requirements, check out TensorFlow models on the Edge TPU.
I tried to export the saved_model for tensorflow serving by
python object_detection/export_inference_graph.py \
--input_type encoded_image_string_tensor \
--pipeline_config_path ${OBJECT_DETECTION_CONFIG} \
--trained_checkpoint_prefix ${YOUR_LOCAL_CHK_DIR}/model.ckpt-${CHECKPOINT_NUMBER} \
--output_directory ${YOUR_LOCAL_EXPORT_DIR}
But it exported an empty folder for the variables.
How to modify the code to make it work?
hii any idea to convert retrain image classifier for use with tensorflow js
from
https://www.tensorflow.org/hub/tutorials/image_retraining
mkdir ~/example_code
cd ~/example_code
curl -LO https://github.com/tensorflow/hub/raw/r0.1/examples/image_retraining/retrain.py
python retrain.py --image_dir ~/flower_photos
try to convert model using tensorflowjs_converter
https://github.com/tensorflow/tfjs-converter
tensorflowjs_converter \
--input_format=tf_frozen_model \
--output_node_names='MobilenetV1/Predictions/Reshape_1' \
--saved_model_tags=serve \
/tmp/output_graph.pb \
/tmp/web_model
getting this error
"graph." % repr(name))
KeyError: "The name 'MobilenetV1/Predictions/Reshape_1' refers to an Operation not in the graph."
also fail for mobilenet v1 model generate using command
python retrain.py \
--image_dir ~/flower_photos \
--tfhub_module https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/feature_vector/1
thanks
#Mustafa I think you're giving the wrong value in the --output_node_names , try going through the model using tensorboard and you will find the value what has to be given here, it should be something like final_result(this is in my case).
I want to train the NMT model from Google on Google Cloud ML.
NMT Model
Now I put all input data in a bucket and downloaded the git repository.
The model needs the nightly version of tensorflow so I defined it in setup.py and when I use the cpu version tf-nightly==1.5.0-dev20171115 and run the following command to run it in GCP local it works.
Train local on google:
gcloud ml-engine local train --package-path nmt/ \
--module-name nmt.nmt \
-- --src=en --tgt=de \
--hparams_path=$HPARAMAS_PATH \
--out_dir=$OUTPUT_DIR \
--vocab_prefix=$VOCAB_PREFIX \
--train_prefix=$TRAIN_PREFIX \
--dev_prefix=$DEV_PREFIX \
--test_prefix=$TEST_PREFIX
Now when I use the gpu version with the following command I got this error message few minutes after submitting the job.
Train on cloud
gcloud ml-engine jobs submit training $JOB_NAME \
--runtime-version 1.2 \
--job-dir $JOB_DIR \
--package-path nmt/ \
--module-name nmt.nmt \
--scale-tier BAISC_GPU \
--region $REGION \
-- --src=en --tgt=de \
--hparams_path=$HPARAMAS_PATH \
--out_dir=$OUTPUT_DIR \
--vocab_prefix=$VOCAB_PREFIX \
--train_prefix=$TRAIN_PREFIX \
--dev_prefix=$DEV_PREFIX \
--test_prefix=$TEST_PREFIX
Error:
import tensorflow as tf ImportError: No module named tensorflow
setup.py:
from setuptools import find_packages
from setuptools import setup
REQUIRED_PACKAGES = ['tf-nightly-gpu==1.5.0-dev20171115']
setup(
name="nmt",
install_requires=REQUIRED_PACKAGES,
packages=find_packages(),
include_package_data=True,
version='0.1.2'
)
Thank you all in advance
Markus
Update:
I have found a note on
GCP docs
Note: Training with TensorFlow versions 1.3+ is limited to CPUs only. See the Cloud ML Engine release notes for updates.
So it seems to doesn't work currently I think I have to go with the compute engine.
Or is there any hack to got it working?
However thank you for your help
The TensorFlow 1.5 might need newer version of CUDA (i.e., CUDA 9), and but the version CloudML Engine installed is CUDA 8. Can you please try to use TensorFlow 1.4 instead, which works on CUDA 8? Please tell us if 1.4 works for you here or send us an email via cloudml-feedback#google.com