GitLab

bug fix and python3 compatability

fixed bug and introduced python3 compatability

* Add --use_synthetic_data option to NCF.

* Add comment to _SYNTHETIC_BATCHES_PER_EPOCH

* Fix test

* Hopefully fix lint issue

Rollback AUTOTUNE.

Use AUTOTUNE, remove noop take, and comment fixes

Open source `Searching for Efficient Multi-Scale Architectures for Dense Image Prediction`

Open source checkpoints for mobilenetv2_dm05_coco_voc_trainaug and mobilenetv2_dm05_coco_voc_trainval

* improve default handling for eval_batch_size

* return eval_batch_size default to None

* fix syntax error

* Update resnet README with new checkpoints and SavedModels

* add more detail on channels_first vs channels_last

* fix typo

* add disclaimer about checkpoints

* set strip_default_attrs=True for SavedModel exports

* specify dtype in resnet export

* another dtype fix

* fix another dtype issue, and set --image_bytes_as_serving_input to default to False

Update installation.md

link to non-deprecated imagenet preprocessing script

Fix cvt_text citation.

Open-sourcing the checkpoint so that users could reproduce our PASCAL VOC 2012 validation set result when training on train_aug set.

* move evaluation from numpy to tensorflow

fix syntax error

don't use sigmoid to convert logits. there is too much precision loss.

WIP: add logit metrics

continue refactor of NCF evaluation

fix syntax error

fix bugs in eval loss calculation

fix eval loss reweighting

remove numpy based metric calculations

fix logging hooks

fix sigmoid to softmax bug

fix comment

catch rare PIPE error and address some PR comments

* fix metric test and address PR comments

* delint and fix python2

* fix test and address PR comments

* extend eval to TPUs

Project import generated by Copybara.

Enabling prediction in mnist_tpu.

Some changes specific to prediction. Removing traces of expected results, as this is just prediction.

Update CloudML job counts in running_pets.md

adding note only SSD models are supported

with serving signature that accepts JPEG image bytes instead
of a fixed size [HxWxC] image tensor.

Passing JPEG image bytes is easier for inference/serving use
cases. The model internally resizes/crops the JPEG image to
required [HxWxC] tensor before passing it on for actual model
inference.

This change aligns with Cloud TPU/ResNet-50 model that offers a
similar interface (jpeg bytes) for inferencing here:

https://github.com/tensorflow/tpu/tree/master/models/official/resnet

NOTE: This flag is set to `True` by default for ImageNet, and is
disallowed for CIFAR (as it does not apply to CIFAR).

PiperOrigin-RevId: 215004158

* Merged commit includes the following changes:
213899768  by Sergio Guadarrama:

    Fixes #3819.

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213493831  by Sergio Guadarrama:

    Internal change

212057654  by Sergio Guadarrama:

    Internal change

210747685  by Sergio Guadarrama:

    For FPN, when use_depthwise is set to true, use slightly modified mobilenet v1 config.

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210128931  by Sergio Guadarrama:

    Allow user-defined current_step in NASNet.

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209092664  by Sergio Guadarrama:

    Add quantized fine-tuning / training / eval and export to slim image classifier binaries.

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207651347  by Sergio Guadarrama:

    Update mobilenet v1 docs to include revised tflite models.

--
207165245  by Sergio Guadarrama:

    Internal change

207095064  by Sergio Guadarrama:

    Internal change

PiperOrigin-RevId: 213899768

* Update model_lib.py to fix eval_spec name issue.

Right now we don't have input data for prediction. So using top 10
entries of test data as input.

Added cvt_text model

save people the trouble of training a model and trying to convert only to realize they should have used SSD model