GitLab

Internal cleanup (py2->py3) plus the following changes:

285513318  by Sergio Guadarrama:

    Adds a script for post-training quantization

284222305  by Sergio Guadarrama:

    Modified squeeze-excite operation to accommodate tensors of undefined (Nonetype) H/W.

282028343  by Sergio Guadarrama:

    Add MobilenetV3 and MobilenetEdgeTPU to the slim/nets_factory.

PiperOrigin-RevId: 289455329
Co-authored-by: Sergio Guadarrama <sguada@gmail.com>

279978375  by Sergio Guadarrama:

    Pass s=2 to the expanded_conv block so it can apply residual correctly in case of fused convolutions.  (Before it was relying on channel mismatch only)

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279788358  by Sergio Guadarrama:

    Update README to add mobilenet-edgetpu details

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279774392  by Sergio Guadarrama:

    Adds MobilenetV3-EdgeTpu definition.

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278917344  by Sergio Guadarrama:

    Create visualwakewords dataset using slim scripts instead of custom scripts.

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277940048  by Sergio Guadarrama:

    Internal changes to tf.contrib symbols

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PiperOrigin-RevId: 279978375

275538818  by Sergio Guadarrama:

    Support grayscale input images in Slim model training

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275355841  by Sergio Guadarrama:

    Fixed cases where tf.TensorShape was constructed with float dimensions

    This is a prerequisite for making TensorShape and Dimension more strict
    about the types of their arguments.

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275131829  by Sergio Guadarrama:

    updates mobilenet/README.md to be github compatible adds V2+ reference to mobilenet_v1.md file and fixes invalid markdown

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PiperOrigin-RevId: 275538818

* 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.

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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.

2. Flag that allows to prevent imagenet.py  from downloading label_to_names from github and/or dumping into training directory (which might be read-only)
3. Adds some comments about how decay steps are computed, since it computed differently when there are clones vs sync replicas.
4. Updates mobilenet.md to describe the training process using train_image_classifer
5. Add citation for TF-Slim model library.

PiperOrigin-RevId: 191955231

PiperOrigin-RevId: 193254125

PiperOrigin-RevId: 193371562

PiperOrigin-RevId: 194085628

PiperOrigin-RevId: 194857067

PiperOrigin-RevId: 196125653

PiperOrigin-RevId: 196589070

PiperOrigin-RevId: 199522873

PiperOrigin-RevId: 200351305

* Make codes more pythonic.

* Restore the indents

Restore the indents.

* Adding option for one_box_for_all_classes to the box_predictor

PiperOrigin-RevId: 192813444

* Extend to accept different ratios of conv channels.

PiperOrigin-RevId: 192837477

* Remove inaccurate caveat from proto file.

PiperOrigin-RevId: 192850747

* Add option to set dropout for classification net in weight shared box predictor.

PiperOrigin-RevId: 192922089

* fix flakiness in testSSDRandomCropWithMultiClassScores due to randomness.

PiperOrigin-RevId: 193067658

* Post-process now works again in train mode.

PiperOrigin-RevId: 193087707

* Adding support for reading in logits as groundtruth labels and applying an optional temperature (scaling) before softmax in support of distillation.

PiperOrigin-RevId: 193119411

* Add a util function to visualize value histogram as a tf.summary.image.

PiperOrigin-RevId: 193137342

* Do not add batch norm parameters to final conv2d ops that predict boxes encodings and class scores in weight shared conv box predictor.

This allows us to set proper bias and force initial predictions to be background when using focal loss.

PiperOrigin-RevId: 193204364

* Make sure the final layers are also resized proportional to conv_depth_ratio.

PiperOrigin-RevId: 193228972

* Remove deprecated batch_norm_trainable field from ssd mobilenet v2 config

PiperOrigin-RevId: 193244778

* Updating coco evaluation metrics to allow for a batch of image info, rather than a single image.

PiperOrigin-RevId: 193382651

* Update protobuf requirements to 3+ in installation docs.

PiperOrigin-RevId: 193409179

* Add support for training keypoints.

PiperOrigin-RevId: 193576336

* Fix data augmentation functions.

PiperOrigin-RevId: 193737238

* Read the default batch size from config file.

PiperOrigin-RevId: 193959861

* Fixing a bug in the coco evaluator.

PiperOrigin-RevId: 193974479

* num_gt_boxes_per_image and num_det_boxes_per_image value incorrect.
Should be not the expand dim.

PiperOrigin-RevId: 194122420

* Add option to evaluate any checkpoint (without requiring write access to that directory and overwriting any existing logs there).

PiperOrigin-RevId: 194292198

* PiperOrigin-RevId: 190346687

* - Expose slim arg_scope function to compute keys to enable tessting.
- Add is_training=None option to mobinenet arg_scopes. This allows the users to set is_training from an outer scope.

PiperOrigin-RevId: 190997959

* Add an option to not set slim arg_scope for batch_norm is_training parameter. This enables users to set the is_training parameter from an outer scope.

PiperOrigin-RevId: 191611934

* PiperOrigin-RevId: 191955231

* PiperOrigin-RevId: 193254125

* PiperOrigin-RevId: 193371562

* PiperOrigin-RevId: 194085628

* fix issue 'could not satisfy explicit device'

* remove the line unrelated to fix issue

This patch assigns dequeue node to inputs_device. And nolonger shows
"Ignoring device specification /device:GPU:X for node
'clone_X/fifo_queue_Dequeue'" message.

Variable summaries and the learning rate are added elsewhere in the
code.  A quick search also shows that this function is never called.

The flag description for the momentum flag states that it is `The
momentum for the MomentumOptimizer and RMSPropOptimizer`, however its
not actually used in the RMSPropOptimizer.  Instead, a separate
`rmsprop_momentum` flag was used.  This deletes that flag for
simplicity.  It was not referenced anywhere else in the repo.