[converter] fixed py import for auto tracking (#7982)

* update py import for auto tracking

* [e2e] Use ios12 and report karma seed (#7984)

* [e2e] Use ios12 and report karma seed

* Update karma.conf.js

---------

Co-authored-by: Matthew Soulanille <[email protected]>

Author: pyu10055

e2e/integration_tests/convert_predict.py

@@ -42,7 +42,7 @@
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import tensor_spec
 from tensorflow.python.ops import variables
-from tensorflow.python.training.tracking import tracking
+from tensorflow.python.trackable import autotrackable
 from tensorflow.python.saved_model.save import save
 import tensorflow_hub as hub
 import tensorflowjs as tfjs
@@ -182,7 +182,7 @@ def _create_saved_model_v2(save_dir):
     save_dir: directory name of where the saved model will be stored.
   """
   input_data = constant_op.constant(1., shape=[1])
-  root = tracking.AutoTrackable()
+  root = autotrackable.AutoTrackable()
   root.v1 = variables.Variable(3.)
   root.v2 = variables.Variable(2.)
   root.f = def_function.function(lambda x: root.v1 * root.v2 * x)
@@ -211,7 +211,7 @@ def square_if_positive(v):
       v = v + 1
     return v
 
-  root = tracking.AutoTrackable()
+  root = autotrackable.AutoTrackable()
   root.f = square_if_positive
   to_save = root.f.get_concrete_function(
       tensor_spec.TensorSpec([], dtypes.float32))
@@ -290,7 +290,7 @@ def _create_saved_model_v2_complex64(save_dir):
     save_dir: directory name of where the saved model will be stored.
   """
   input_data = constant_op.constant(1., shape=[1])
-  root = tracking.AutoTrackable()
+  root = autotrackable.AutoTrackable()
   root.v1 = variables.Variable(3 + 1j, dtype=tf.complex64)
   root.f = def_function.function(lambda x: tf.complex(x, x) + root.v1)
   to_save = root.f.get_concrete_function(input_data)

e2e/integration_tests/metadata.py

@@ -42,7 +42,7 @@
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import tensor_spec
 from tensorflow.python.ops import variables
-from tensorflow.python.training.tracking import tracking
+from tensorflow.python.trackable import autotrackable
 from tensorflow.python.saved_model.save import save
 import tensorflow_hub as hub
 import tensorflowjs as tfjs
@@ -79,7 +79,7 @@ def _create_model_with_metadata():
 
 def _create_saved_model(save_dir):
   input_data = constant_op.constant(1., shape=[1])
-  root = tracking.AutoTrackable()
+  root = autotrackable.AutoTrackable()
   root.v1 = variables.Variable(3.)
   root.v2 = variables.Variable(2.)
   root.f = def_function.function(lambda x: root.v1 * root.v2 * x)

e2e/karma.conf.js

@@ -144,6 +144,10 @@ module.exports = function(config) {
 
   config.set({
     ...extraConfig,
+    reporters: [
+      'spec',
+      'jasmine-order',
+    ],
     browsers: ['Chrome'],
     browserStack: {
       username: process.env.BROWSERSTACK_USERNAME,
@@ -182,9 +186,9 @@ module.exports = function(config) {
       },
       bs_ios_12: {
         base: 'BrowserStack',
-        device: 'iPhone X',
-        os: 'iOS',
-        os_version: '11.0',
+        device: 'iPhone XS',
+        os: 'ios',
+        os_version: '12.3',
         real_mobile: true
       },
       bs_android_10: {

e2e/package.json

@@ -30,6 +30,7 @@
     "karma-browserstack-launcher": "~1.6.0",
     "karma-chrome-launcher": "~3.1.1",
     "karma-jasmine": "~1.1.0",
+    "karma-jasmine-order-reporter": "^1.1.0",
     "karma-spec-reporter": "~0.0.32",
     "karma-typescript": "~5.5.1",
     "karma-typescript-es6-transform": "^5.1.0",

e2e/yarn.lock

@@ -2855,10 +2855,15 @@ karma-chrome-launcher@~3.1.1:
   dependencies:
     which "^1.2.1"
 
+karma-jasmine-order-reporter@^1.1.0:
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/karma-jasmine-order-reporter/-/karma-jasmine-order-reporter-1.1.0.tgz#3bd4cfd9e5ae53cc0d972905b811a479c127d0a8"
+  integrity sha512-mzC6WfOg7y5kCf7K4W+nB5NHea1ogcDjRpepFLLRjbi4lx08wQG+6EY2zd+mC2ANIvb6+ux2d9mFVtZ3MIwTYg==
+
 karma-jasmine@~1.1.0:
   version "1.1.2"
   resolved "https://registry.yarnpkg.com/karma-jasmine/-/karma-jasmine-1.1.2.tgz#394f2b25ffb4a644b9ada6f22d443e2fd08886c3"
-  integrity sha1-OU8rJf+0pkS5rabyLUQ+L9CIhsM=
+  integrity sha512-SENGE9DhlIIFTSZWiNq4eGeXL8G6z9cqHIOdkx9jh1qhhQqwEy3tAoLRyER0vOcHqdOlKmGpOuXk+HOipIy7sg==
 
 karma-spec-reporter@~0.0.32:
   version "0.0.33"

tfjs-converter/python/tensorflowjs/converters/fuse_prelu_test.py

@@ -24,7 +24,7 @@
 from tensorflow.python.eager import def_function
 from tensorflow.python.framework import constant_op
 from tensorflow.python.ops import variables
-from tensorflow.python.training.tracking import tracking
+from tensorflow.python.trackable import autotrackable
 
 from tensorflowjs.converters import fuse_depthwise_conv2d
 from tensorflowjs.converters import fuse_prelu
@@ -242,7 +242,7 @@ def execute_model(tensor):
   def testNonPreluPattern(self):
     """Test a basic model with functions to make sure functions are inlined."""
     input_data = constant_op.constant(1., shape=[1])
-    root = tracking.AutoTrackable()
+    root = autotrackable.AutoTrackable()
     root.v1 = variables.Variable(3.)
     root.v2 = variables.Variable(2.)
 

tfjs-converter/python/tensorflowjs/converters/tf_saved_model_conversion_v2_test.py

@@ -30,7 +30,7 @@
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import tensor_spec
 from tensorflow.python.ops import variables
-from tensorflow.python.training.tracking import tracking
+from tensorflow.python.trackable import autotrackable
 from tensorflow.python.tools import freeze_graph
 from tensorflow.python.saved_model.save import save
 import tensorflow_hub as hub
@@ -190,7 +190,7 @@ def conv2d_addV2_depthwise_addV2(x):
         input=res, filter=filter, strides=[1, 1, 1, 1], padding="VALID")
       res = tf.raw_ops.AddV2(x=res, y=bias)
       return res
-    root = tracking.AutoTrackable()
+    root = autotrackable.AutoTrackable()
     root.f = conv2d_addV2_depthwise_addV2
     to_save = root.f.get_concrete_function(
         tensor_spec.TensorSpec([1, 1, 1, 1], dtypes.float32))
@@ -207,7 +207,7 @@ def addV2_conv2d(x):
       res = tf.raw_ops.Conv2D(
         input=res, filter=filter, strides=[1, 1, 1, 1], padding="VALID")
       return res
-    root = tracking.AutoTrackable()
+    root = autotrackable.AutoTrackable()
     root.f = addV2_conv2d
     to_save = root.f.get_concrete_function(
         tensor_spec.TensorSpec([1, 1, 1, 1], dtypes.float32))
@@ -247,7 +247,7 @@ def _create_saved_model_with_unfusable_prelu(self):
   def _create_saved_model(self):
     """Test a basic model with functions to make sure functions are inlined."""
     input_data = constant_op.constant(1., shape=[1])
-    root = tracking.AutoTrackable()
+    root = autotrackable.AutoTrackable()
     root.v1 = variables.Variable(3.)
     root.v2 = variables.Variable(2.)
     root.f = def_function.function(lambda x: root.v1 * root.v2 * x)
@@ -260,7 +260,7 @@ def _create_saved_model_with_fusable_matmul(self):
     """Test a fusable matmul model."""
     input_data = constant_op.constant(1., shape=[1, 1])
     bias_data = constant_op.constant(1., shape=[1])
-    root = tracking.AutoTrackable()
+    root = autotrackable.AutoTrackable()
     root.v2 = variables.Variable([[2.]])
     root.f = def_function.function(
         lambda x: tf.nn.relu(tf.nn.bias_add(tf.matmul(x, root.v2),
@@ -278,7 +278,7 @@ def find_next_odd(v):
       while tf.equal(v1 % 2, 0):
         v1 = v1 + 1
       return v1
-    root = tracking.AutoTrackable()
+    root = autotrackable.AutoTrackable()
     root.f = find_next_odd
     to_save = root.f.get_concrete_function(
         tensor_spec.TensorSpec([], dtypes.int32))
@@ -303,7 +303,7 @@ def _create_saved_model_with_tfdf(self):
     model.save(save_dir)
 
   def _create_unsupported_saved_model(self):
-    root = tracking.AutoTrackable()
+    root = autotrackable.AutoTrackable()
     root.w = variables.Variable(tf.random.uniform([2, 2]))
 
     @def_function.function
@@ -322,7 +322,7 @@ def exported_function(x):
     save(root, save_dir, to_save)
 
   def _create_saved_model_with_debug_ops(self):
-    root = tracking.AutoTrackable()
+    root = autotrackable.AutoTrackable()
     root.w = variables.Variable(tf.random.uniform([2, 2]))
 
     @def_function.function

tfjs-converter/python/tensorflowjs/converters/wizard_test.py

@@ -24,7 +24,7 @@
 import tensorflow.compat.v2 as tf
 from tensorflow.python.eager import def_function
 from tensorflow.python.ops import variables
-from tensorflow.python.training.tracking import tracking
+from tensorflow.python.trackable import autotrackable
 from tensorflow.python.saved_model import save
 
 from tensorflowjs.converters import wizard
@@ -74,7 +74,7 @@ def _create_keras_saved_model(self):
   def _create_saved_model(self):
     """Test a basic model with functions to make sure functions are inlined."""
     input_data = tf.constant(1., shape=[1])
-    root = tracking.AutoTrackable()
+    root = autotrackable.AutoTrackable()
     root.v1 = variables.Variable(3.)
     root.v2 = variables.Variable(2.)
     root.f = def_function.function(lambda x: root.v1 * root.v2 * x)

tfjs-converter/python/test_pip_package.py

@@ -33,7 +33,7 @@
 from tensorflow.python.framework import constant_op
 from tensorflow.python.ops import variables
 from tensorflow.python.tools import freeze_graph
-from tensorflow.python.training.tracking import tracking
+from tensorflow.python.trackable import autotrackable
 from tensorflow.python.saved_model.save import save
 import tensorflow_hub as hub
 
@@ -115,7 +115,7 @@ def _createTensorFlowSavedModel(save_path):
   """
 
   input_data = constant_op.constant(1., shape=[1])
-  root = tracking.AutoTrackable()
+  root = autotrackable.AutoTrackable()
   root.v1 = variables.Variable(3.)
   root.v2 = variables.Variable(2.)
   root.f = def_function.function(lambda x: root.v1 * root.v2 * x)

tfjs-node/python/int64_model.py

@@ -21,12 +21,12 @@
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import variables
-from tensorflow.python.training.tracking import tracking
+from tensorflow.python.trackable import autotrackable
 from tensorflow.python.saved_model.save import save
 
 """Test a basic model with functions to make sure functions are inlined."""
 input_data = constant_op.constant(1, shape=[2], dtype=tf.int64)
-root = tracking.AutoTrackable()
+root = autotrackable.AutoTrackable()
 root.v1 = variables.Variable(3, dtype=tf.int64)
 root.v2 = variables.Variable(2, dtype=tf.int64)
 root.f = def_function.function(lambda x: root.v1 * root.v2 * x)