Help protect the Great Barrier Reef with TensorFlow on Kaggle Join Challenge

Automatically rewrite TF 1.x and compat.v1 API symbols

View on TensorFlow.org Run in Google Colab View source on GitHub Download notebook

TensorFlow 2.x includes many API changes from TF 1.x and the tf.compat.v1 APIs, such as reordering arguments, renaming symbols, and changing default values for parameters. Manually performing all of these modifications would be tedious and prone to error. To streamline the changes, and to make your transition to TF 2.x as seamless as possible, the TensorFlow team has created the tf_upgrade_v2 utility to help transition legacy code to the new API.

Typical usage is like this:

tf_upgrade_v2 \
  --intree my_project/ \
  --outtree my_project_v2/ \
  --reportfile report.txt

It will accelerate your upgrade process by converting existing TensorFlow 1.x Python scripts to TensorFlow 2.x.

The conversion script automates many mechanical API transformations, though many APIs cannot be automatically migrated. It is also not able to fully make your code compatible with TF2 behaviors and APIs. So, it is only a part of your migration journey.

Compatibility modules

Certain API symbols can not be upgraded simply by using a string replacement. Those that cannot be automatically upgraded will be mapped to their locations in the compat.v1 module. This module replaces TF 1.x symbols like tf.foo with the equivalent tf.compat.v1.foo reference. If you are already using compat.v1 APIs by importing TF via import tensorflow.compat.v1 as tf, the tf_upgrade_v2 script will attempt to convert these usages to the non-compat APIs where possible. Note that while some compat.v1 APIs are compatible with TF2.x behaviors, many are not. So, we recommend that you manually proofread replacements and migrate them to new APIs in the tf.* namespace instead of tf.compat.v1 namespace as quickly as possible.

Because of TensorFlow 2.x module deprecations (for example, tf.flags and tf.contrib), some changes can not be worked around by switching to compat.v1. Upgrading this code may require using an additional library (for example, absl.flags) or switching to a package in tensorflow/addons.

The rest of this guide demonstrates how to use the symbol-rewriting script. While the script is easy to use, it is strongly recomended that you use the script as part of the following process:

  1. Unit Test: Ensure that the code you’re upgrading has a unit test suite with reasonable coverage. This is Python code, so the language won’t protect you from many classes of mistakes. Also ensure that any dependency you have has already been upgraded to be compatible with TensorFlow 2.x.

  2. Install TensorFlow 1.15: Upgrade your TensorFlow to the latest TensorFlow 1.x version, at least 1.15. This includes the final TensorFlow 2.0 API in tf.compat.v2.

  3. Test With 1.15: Ensure your unit tests pass at this point. You’ll be running them repeatedly as you upgrade so starting from green is important.

  4. Run the upgrade script: Run tf_upgrade_v2 on your entire source tree, tests included. This will upgrade your code to a format where it only uses symbols available in TensorFlow 2.0. Deprecated symbols will be accessed with tf.compat.v1. These will eventually require manual attention, but not immediately.

  5. Run the converted tests with TensorFlow 1.15: Your code should still run fine in TensorFlow 1.15. Run your unit tests again. Any error in your tests here means there’s a bug in the upgrade script. Please let us know.

  6. Check the upgrade report for warnings and errors: The script writes a report file that explains any conversions you should double-check, or any manual action you need to take. For example: Any remaining instances of contrib will require manual action to remove. Please consult the RFC for more instructions.

  7. Install TensorFlow 2.x: At this point it should be safe to switch to TensorFlow 2.x binaries, even if you are running with legacy behaviors

  8. Test with v1.disable_v2_behavior: Re-running your tests with a v1.disable_v2_behavior() in the tests' main function should give the same results as running under 1.15.

  9. Enable V2 Behavior: Now that your tests work using the TF2 binaries, you can now begin migrating your code to avoiding tf.estimators and only using supported TF2 behaviors (with no TF2 behavior disabling). See the Migration guides for details.

Using the symbol-rewriting tf_upgrade_v2 script

Setup

Before getting started ensure that TensorFlow 2.x is installed.

import tensorflow as tf

print(tf.__version__)
2.6.0

Clone the tensorflow/models git repository so you have some code to test on:

git clone --branch r1.13.0 --depth 1 https://github.com/tensorflow/models
Cloning into 'models'...
remote: Enumerating objects: 2927, done.[K
remote: Counting objects: 100% (2927/2927), done.[K
remote: Compressing objects: 100% (2428/2428), done.[K
remote: Total 2927 (delta 504), reused 2113 (delta 424), pack-reused 0[K
Receiving objects: 100% (2927/2927), 369.04 MiB | 27.58 MiB/s, done.
Resolving deltas: 100% (504/504), done.
Checking out files: 100% (2768/2768), done.

Read the help

The script should be installed with TensorFlow. Here is the builtin help:

tf_upgrade_v2 -h
usage: tf_upgrade_v2 [-h] [--infile INPUT_FILE] [--outfile OUTPUT_FILE]
                     [--intree INPUT_TREE] [--outtree OUTPUT_TREE]
                     [--copyotherfiles COPY_OTHER_FILES] [--inplace]
                     [--no_import_rename] [--no_upgrade_compat_v1_import]
                     [--reportfile REPORT_FILENAME] [--mode {DEFAULT,SAFETY}]
                     [--print_all]

Convert a TensorFlow Python file from 1.x to 2.0

Simple usage:
  tf_upgrade_v2.py --infile foo.py --outfile bar.py
  tf_upgrade_v2.py --infile foo.ipynb --outfile bar.ipynb
  tf_upgrade_v2.py --intree ~/code/old --outtree ~/code/new

optional arguments:
  -h, --help            show this help message and exit
  --infile INPUT_FILE   If converting a single file, the name of the file to
                        convert
  --outfile OUTPUT_FILE
                        If converting a single file, the output filename.
  --intree INPUT_TREE   If converting a whole tree of files, the directory to
                        read from (relative or absolute).
  --outtree OUTPUT_TREE
                        If converting a whole tree of files, the output
                        directory (relative or absolute).
  --copyotherfiles COPY_OTHER_FILES
                        If converting a whole tree of files, whether to copy
                        the other files.
  --inplace             If converting a set of files, whether to allow the
                        conversion to be performed on the input files.
  --no_import_rename    Not to rename import to compat.v2 explicitly.
  --no_upgrade_compat_v1_import
                        If specified, don't upgrade explicit imports of
                        `tensorflow.compat.v1 as tf` to the v2 APIs.
                        Otherwise, explicit imports of the form
                        `tensorflow.compat.v1 as tf` will be upgraded.
  --reportfile REPORT_FILENAME
                        The name of the file where the report log is
                        stored.(default: report.txt)
  --mode {DEFAULT,SAFETY}
                        Upgrade script mode. Supported modes: DEFAULT: Perform
                        only straightforward conversions to upgrade to 2.0. In
                        more difficult cases, switch to use compat.v1. SAFETY:
                        Keep 1.* code intact and import compat.v1 module.
  --print_all           Print full log to stdout instead of just printing
                        errors

Example TF1 code

Here is a simple TensorFlow 1.0 script:

head -n 65 models/samples/cookbook/regression/custom_regression.py | tail -n 10
# Calculate loss using mean squared error
  average_loss = tf.losses.mean_squared_error(labels, predictions)

  # Pre-made estimators use the total_loss instead of the average,
  # so report total_loss for compatibility.
  batch_size = tf.shape(labels)[0]
  total_loss = tf.to_float(batch_size) * average_loss

  if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = params.get("optimizer", tf.train.AdamOptimizer)

With TensorFlow 2.x installed it does not run:

(cd models/samples/cookbook/regression && python custom_regression.py)
Traceback (most recent call last):
  File "custom_regression.py", line 162, in <module>
    tf.logging.set_verbosity(tf.logging.INFO)
AttributeError: module 'tensorflow' has no attribute 'logging'

Single file

The script can be run on a single Python file:

!tf_upgrade_v2 \
  --infile models/samples/cookbook/regression/custom_regression.py \
  --outfile /tmp/custom_regression_v2.py
INFO line 38:8: Renamed 'tf.feature_column.input_layer' to 'tf.compat.v1.feature_column.input_layer'
INFO line 43:10: Renamed 'tf.layers.dense' to 'tf.compat.v1.layers.dense'
INFO line 46:17: Renamed 'tf.layers.dense' to 'tf.compat.v1.layers.dense'
INFO line 57:17: tf.losses.mean_squared_error requires manual check. tf.losses have been replaced with object oriented versions in TF 2.0 and after. The loss function calls have been converted to compat.v1 for backward compatibility. Please update these calls to the TF 2.0 versions.
INFO line 57:17: Renamed 'tf.losses.mean_squared_error' to 'tf.compat.v1.losses.mean_squared_error'
INFO line 61:15: Added keywords to args of function 'tf.shape'
INFO line 62:15: Changed tf.to_float call to tf.cast(..., dtype=tf.float32).
INFO line 65:40: Renamed 'tf.train.AdamOptimizer' to 'tf.compat.v1.train.AdamOptimizer'
INFO line 68:39: Renamed 'tf.train.get_global_step' to 'tf.compat.v1.train.get_global_step'
INFO line 83:9: tf.metrics.root_mean_squared_error requires manual check. tf.metrics have been replaced with object oriented versions in TF 2.0 and after. The metric function calls have been converted to compat.v1 for backward compatibility. Please update these calls to the TF 2.0 versions.
INFO line 83:9: Renamed 'tf.metrics.root_mean_squared_error' to 'tf.compat.v1.metrics.root_mean_squared_error'
INFO line 142:23: Renamed 'tf.train.AdamOptimizer' to 'tf.compat.v1.train.AdamOptimizer'
INFO line 162:2: Renamed 'tf.logging.set_verbosity' to 'tf.compat.v1.logging.set_verbosity'
INFO line 162:27: Renamed 'tf.logging.INFO' to 'tf.compat.v1.logging.INFO'
INFO line 163:2: Renamed 'tf.app.run' to 'tf.compat.v1.app.run'
TensorFlow 2.0 Upgrade Script
-----------------------------
Converted 1 files
Detected 0 issues that require attention
--------------------------------------------------------------------------------


Make sure to read the detailed log 'report.txt'

The script will print errors if it can not find a fix for the code.

Directory tree

Typical projects, including this simple example, will use much more than one file. Typically want to update an entire package, so the script can also be run on a directory tree:

# update the .py files and copy all the other files to the outtree
!tf_upgrade_v2 \
    --intree models/samples/cookbook/regression/ \
    --outtree regression_v2/ \
    --reportfile tree_report.txt
INFO line 82:10: tf.estimator.LinearRegressor: Default value of loss_reduction has been changed to SUM_OVER_BATCH_SIZE; inserting old default value tf.keras.losses.Reduction.SUM.

INFO line 105:2: Renamed 'tf.logging.set_verbosity' to 'tf.compat.v1.logging.set_verbosity'
INFO line 105:27: Renamed 'tf.logging.INFO' to 'tf.compat.v1.logging.INFO'
INFO line 106:2: Renamed 'tf.app.run' to 'tf.compat.v1.app.run'
INFO line 38:8: Renamed 'tf.feature_column.input_layer' to 'tf.compat.v1.feature_column.input_layer'
INFO line 43:10: Renamed 'tf.layers.dense' to 'tf.compat.v1.layers.dense'
INFO line 46:17: Renamed 'tf.layers.dense' to 'tf.compat.v1.layers.dense'
INFO line 57:17: tf.losses.mean_squared_error requires manual check. tf.losses have been replaced with object oriented versions in TF 2.0 and after. The loss function calls have been converted to compat.v1 for backward compatibility. Please update these calls to the TF 2.0 versions.
INFO line 57:17: Renamed 'tf.losses.mean_squared_error' to 'tf.compat.v1.losses.mean_squared_error'
INFO line 61:15: Added keywords to args of function 'tf.shape'
INFO line 62:15: Changed tf.to_float call to tf.cast(..., dtype=tf.float32).
INFO line 65:40: Renamed 'tf.train.AdamOptimizer' to 'tf.compat.v1.train.AdamOptimizer'
INFO line 68:39: Renamed 'tf.train.get_global_step' to 'tf.compat.v1.train.get_global_step'
INFO line 83:9: tf.metrics.root_mean_squared_error requires manual check. tf.metrics have been replaced with object oriented versions in TF 2.0 and after. The metric function calls have been converted to compat.v1 for backward compatibility. Please update these calls to the TF 2.0 versions.
INFO line 83:9: Renamed 'tf.metrics.root_mean_squared_error' to 'tf.compat.v1.metrics.root_mean_squared_error'
INFO line 142:23: Renamed 'tf.train.AdamOptimizer' to 'tf.compat.v1.train.AdamOptimizer'
INFO line 162:2: Renamed 'tf.logging.set_verbosity' to 'tf.compat.v1.logging.set_verbosity'
INFO line 162:27: Renamed 'tf.logging.INFO' to 'tf.compat.v1.logging.INFO'
INFO line 163:2: Renamed 'tf.app.run' to 'tf.compat.v1.app.run'
INFO line 58:10: tf.estimator.LinearRegressor: Default value of loss_reduction has been changed to SUM_OVER_BATCH_SIZE; inserting old default value tf.keras.losses.Reduction.SUM.

INFO line 101:2: Renamed 'tf.logging.set_verbosity' to 'tf.compat.v1.logging.set_verbosity'
INFO line 101:27: Renamed 'tf.logging.INFO' to 'tf.compat.v1.logging.INFO'
INFO line 102:2: Renamed 'tf.app.run' to 'tf.compat.v1.app.run'
INFO line 72:10: tf.estimator.DNNRegressor: Default value of loss_reduction has been changed to SUM_OVER_BATCH_SIZE; inserting old default value tf.keras.losses.Reduction.SUM.

INFO line 96:2: Renamed 'tf.logging.set_verbosity' to 'tf.compat.v1.logging.set_verbosity'
INFO line 96:27: Renamed 'tf.logging.INFO' to 'tf.compat.v1.logging.INFO'
INFO line 97:2: Renamed 'tf.app.run' to 'tf.compat.v1.app.run'
WARNING line 125:15: Changing dataset.make_one_shot_iterator() to tf.compat.v1.data.make_one_shot_iterator(dataset). Please check this transformation.

INFO line 40:7: Renamed 'tf.test.mock' to 'tf.compat.v1.test.mock'
TensorFlow 2.0 Upgrade Script
-----------------------------
Converted 7 files
Detected 1 issues that require attention
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
File: models/samples/cookbook/regression/automobile_data.py
--------------------------------------------------------------------------------
models/samples/cookbook/regression/automobile_data.py:125:15: WARNING: Changing dataset.make_one_shot_iterator() to tf.compat.v1.data.make_one_shot_iterator(dataset). Please check this transformation.



Make sure to read the detailed log 'tree_report.txt'

Note the one warning about the dataset.make_one_shot_iterator function.

Now the script works in with TensorFlow 2.x:

Note that because the tf.compat.v1 module is included in TF 1.15, the converted script will also run in TensorFlow 1.15.

(cd regression_v2 && python custom_regression.py 2>&1) | tail
I0922 22:16:42.778216 140254758430528 estimator.py:2074] Saving dict for global step 1000: global_step = 1000, loss = 651.5428, rmse = 3.684265
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: /tmp/tmpk2_4r192/model.ckpt-1000
I0922 22:16:42.817190 140254758430528 estimator.py:2135] Saving 'checkpoint_path' summary for global step 1000: /tmp/tmpk2_4r192/model.ckpt-1000
Tensor("IteratorGetNext:25", shape=(None,), dtype=float64, device=/device:CPU:0)
Tensor("Squeeze:0", shape=(None,), dtype=float32)

********************************************************************************

RMS error for the test set: $3684

Detailed report

The script also reports a list of detailed changes. In this example it found one possibly unsafe transformation and included a warning at the top of the file:

head -n 20 tree_report.txt
TensorFlow 2.0 Upgrade Script
-----------------------------
Converted 7 files
Detected 1 issues that require attention
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
File: models/samples/cookbook/regression/automobile_data.py
--------------------------------------------------------------------------------
models/samples/cookbook/regression/automobile_data.py:125:15: WARNING: Changing dataset.make_one_shot_iterator() to tf.compat.v1.data.make_one_shot_iterator(dataset). Please check this transformation.

================================================================================
Detailed log follows:

================================================================================
================================================================================
Input tree: 'models/samples/cookbook/regression/'
================================================================================
--------------------------------------------------------------------------------
Processing file 'models/samples/cookbook/regression/__init__.py'
 outputting to 'regression_v2/__init__.py'

Note again the one warning about the Dataset.make_one_shot_iterator function.

In other cases the output will explain the reasoning for non-trivial changes:

%%writefile dropout.py
import tensorflow as tf

d = tf.nn.dropout(tf.range(10), 0.2)
z = tf.zeros_like(d, optimize=False)
Writing dropout.py
!tf_upgrade_v2 \
  --infile dropout.py \
  --outfile dropout_v2.py \
  --reportfile dropout_report.txt > /dev/null
cat dropout_report.txt
TensorFlow 2.0 Upgrade Script
-----------------------------
Converted 1 files
Detected 0 issues that require attention
--------------------------------------------------------------------------------
================================================================================
Detailed log follows:

================================================================================
--------------------------------------------------------------------------------
Processing file 'dropout.py'
 outputting to 'dropout_v2.py'
--------------------------------------------------------------------------------

3:4: INFO: Changing keep_prob arg of tf.nn.dropout to rate, and recomputing value.

4:4: INFO: Renaming tf.zeros_like to tf.compat.v1.zeros_like because argument optimize is present. tf.zeros_like no longer takes an optimize argument, and behaves as if optimize=True. This call site specifies something other than optimize=True, so it was converted to compat.v1.
--------------------------------------------------------------------------------

Here is the modified file contents, note how the script adds argument names to deal with moved and renamed arguments:

cat dropout_v2.py
import tensorflow as tf

d = tf.nn.dropout(tf.range(10), rate=1 - (0.2))
z = tf.compat.v1.zeros_like(d, optimize=False)

A larger project might contain a few errors. For example convert the deeplab model:

!tf_upgrade_v2 \
    --intree models/research/deeplab \
    --outtree deeplab_v2 \
    --reportfile deeplab_report.txt > /dev/null

It produced the output files:

ls deeplab_v2
README.md   datasets        input_preprocess.py        train.py
__init__.py deeplab_demo.ipynb  local_test.sh          utils
common.py   eval.py         local_test_mobilenetv2.sh  vis.py
common_test.py  export_model.py     model.py
core        g3doc           model_test.py

But there were errors. The report will help you pin-point what you need to fix before this will run. Here are the first three errors:

cat deeplab_report.txt | grep -i models/research/deeplab | grep -i error | head -n 3
models/research/deeplab/eval.py:28:7: ERROR: Using member tf.contrib.slim in deprecated module tf.contrib. tf.contrib.slim cannot be converted automatically. tf.contrib will not be distributed with TensorFlow 2.0, please consider an alternative in non-contrib TensorFlow, a community-maintained repository such as tensorflow/addons, or fork the required code.
models/research/deeplab/eval.py:146:8: ERROR: Using member tf.contrib.metrics.aggregate_metric_map in deprecated module tf.contrib. tf.contrib.metrics.aggregate_metric_map cannot be converted automatically. tf.contrib will not be distributed with TensorFlow 2.0, please consider an alternative in non-contrib TensorFlow, a community-maintained repository such as tensorflow/addons, or fork the required code.
models/research/deeplab/export_model.py:25:7: ERROR: Using member tf.contrib.slim in deprecated module tf.contrib. tf.contrib.slim cannot be converted automatically. tf.contrib will not be distributed with TensorFlow 2.0, please consider an alternative in non-contrib TensorFlow, a community-maintained repository such as tensorflow/addons, or fork the required code.

"Safety" mode

The conversion script also has a less invasive SAFETY mode that simply changes the imports to use the tensorflow.compat.v1 module:

cat dropout.py
import tensorflow as tf

d = tf.nn.dropout(tf.range(10), 0.2)
z = tf.zeros_like(d, optimize=False)
tf_upgrade_v2 --mode SAFETY --infile dropout.py --outfile dropout_v2_safe.py > /dev/null
cat dropout_v2_safe.py
import tensorflow.compat.v1 as tf

d = tf.nn.dropout(tf.range(10), 0.2)
z = tf.zeros_like(d, optimize=False)

As you can see this doesn't upgrade your code, but does allow TensorFlow 1 code to run against TensorFlow 2 binaries. Note that this does not mean your code is running supported TF 2.x behaviors!

Caveats

  • Do not update parts of your code manually before running this script. In particular, functions that have had reordered arguments like tf.argmax or tf.batch_to_space cause the script to incorrectly add keyword arguments that mismap your existing code.

  • The script assumes that tensorflow is imported using import tensorflow as tf, or import tensorflow.compat.v1 as tf.

  • This script does not reorder arguments. Instead, the script adds keyword arguments to functions that have their arguments reordered.

  • Check out tf2up.ml for a convenient tool to upgrade Jupyter notebooks and Python files in a GitHub repository.

To report upgrade script bugs or make feature requests, please file an issue on GitHub.