Implementing DetectionPolicy with existing Inceptionv3 model #41
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…g now internal to detectionpolicy
| downloadDir = 'XXX/orig' | ||
| archive_storage_bucket = "fuego-firecam-a" | ||
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| server_ip_and_port = 'localhost:8500' #depends on the specific inference server running on GCP |
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"localhost" means do inference locally on same machine. Can also be an IP address to an inference server
| dataset_dir, split_name, shard_id, numShards) | ||
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| with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer: | ||
| with tf.io.TFRecordWriter(output_filename) as tfrecord_writer: |
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Shouldn't change functionality, just switching from deprecated interface
kinshuk
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kinshuk
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I'm curious to know how the model produced by this compares with our current inception model.
| import datetime | ||
| import math | ||
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| import docker |
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Please add the standard license and copyright header at the top of the file. Also, please add one or few lines comment describing the main purpose of this file.
| # tf.app.flags.DEFINE_string('server', server_ip_and_port, 'PredictionService host:port') | ||
| # channel = grpc.insecure_channel(tf.app.flags.FLAGS.server) |
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Should these two lines be deleted? If not, please describe when/how they are needed/useful.
| # tf.app.flags.DEFINE_string('server', server_ip_and_port, 'PredictionService host:port') | ||
| # channel = grpc.insecure_channel(tf.app.flags.FLAGS.server) | ||
| channel = grpc.insecure_channel(server_ip_and_port) | ||
| # grpc.secure_channel() |
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secure_channel definitely sounds better than insecure_channel. Please document what's preventing using secure version.
| docker pull tensorflow/serving:latest-gpu | ||
| sudo docker run -d --name serving_base tensorflow/serving:latest-gpu | ||
| # create intermediate dir | ||
| sudo docker exec serving_base mkdir -p /models/$NAME | ||
| sudo docker cp $MODEL_PATH serving_base:/models/$NAME/1 | ||
| sudo docker commit --change "ENV MODEL_NAME $NAME" serving_base $NAME"_serving" | ||
| sudo docker kill serving_base | ||
| sudo docker rm serving_base |
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Is this possible from Dockerfile? If so, what's the tradeoff of creating the image using this script vs. Dockerfile?
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| import glob | |||
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Please add the standard license and copyright header at the top of the file. Also, please add one or few lines comment describing the main purpose of this file.
| optArgs = [ | ||
| ["t", "trainPercentage", "percentage of data to use for training vs. validation (default 90)"] | ||
| ] |
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The code doesn't use this argument, so you can just remove this and use an empty list.
| val_steps = 100 #only needed for now because of a bug in tf2.0, which should be fixed in next version | ||
| #TODO: either set this to # of validation examples /batch size (i.e. figure out num validation examples) | ||
| #or upgrade to TF2.1 when its ready and automatically go thorugh the whole set |
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Please reference the bug number/URL in the comment.
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I think these commits that aren't my username were because I forgot to change the default email in git on the VM template we used.