5.2 Release (#1401)

* 5.2 Release

* Fix undefinded variables

* Update supported version number

* Additional 5.2 changes

* Modify .gitlab-ci.yml

.gitlab-ci.yml

@@ -18,7 +18,7 @@ check_python_flake8:
   script:
     - python -m pip install --upgrade pip
     - pip install flake8
-    - flake8 ./coremltools --count --select=E9,F63,F7,F82 --show-source --statistics
+    - flake8 ./coremltools --count --select=E9,F5,F63,F7,F82 --show-source --statistics
 
 ########################################################################
 #
@@ -198,18 +198,6 @@ test_macos11_py37_tf1:
     TEST_PACKAGE: coremltools.converters.mil.frontend.tensorflow
     WHEEL_PATH: build/dist/*cp37*10_15*
 
-test_macos11_py37_tf2:
-  <<: *test_macos_pkg_with_reqs
-  tags:
-    - macos11
-  dependencies:
-    - build_wheel_macos_py37
-  variables:
-    PYTHON: "3.7"
-    REQUIREMENTS: reqs/test_tf2.pip
-    TEST_PACKAGE: coremltools.converters.mil.frontend.tensorflow2
-    WHEEL_PATH: build/dist/*cp37*10_15*
-
 test_macos11_py37_mil:
   <<: *test_macos_pkg
   tags:
@@ -218,7 +206,7 @@ test_macos11_py37_mil:
     - build_wheel_macos_py37
   variables:
     PYTHON: "3.7"
-    TEST_PACKAGE: coremltools.converters.mil
+    TEST_PACKAGE: coremltools.converters.mil.mil
     WHEEL_PATH: build/dist/*cp37*10_15*
 
 #########################################################################

coremltools/_deps/__init__.py

@@ -100,7 +100,7 @@ def __get_sklearn_version(version):
 _TF_1_MIN_VERSION = "1.12.0"
 _TF_1_MAX_VERSION = "1.15.0"
 _TF_2_MIN_VERSION = "2.1.0"
-_TF_2_MAX_VERSION = "2.5.0"
+_TF_2_MAX_VERSION = "2.6.2"
 
 try:
     import tensorflow
@@ -147,7 +147,7 @@ def __get_sklearn_version(version):
 _HAS_KERAS_TF = True
 _HAS_KERAS2_TF = True
 _KERAS_MIN_VERSION = "1.2.2"
-_KERAS_MAX_VERSION = "2.2.4"
+_KERAS_MAX_VERSION = "2.6.0"
 MSG_KERAS1_NOT_FOUND = "Keras 1 not found."
 MSG_KERAS2_NOT_FOUND = "Keras 2 not found."
 
@@ -218,7 +218,7 @@ def __get_sklearn_version(version):
 
 # ---------------------------------------------------------------------------------------
 _HAS_TORCH = True
-_TORCH_MAX_VERSION = "1.9.1"
+_TORCH_MAX_VERSION = "1.10.2"
 try:
     import torch
     _warn_if_above_max_supported_version("Torch", torch.__version__, _TORCH_MAX_VERSION)

coremltools/converters/_converters_entry.py

@@ -2,20 +2,29 @@
 #
 # Use of this source code is governed by a BSD-3-clause license that can be
 # found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
-import gc
+
 import collections
+import gc
+import os
 import warnings
 
-from coremltools import ComputeUnit as _ComputeUnit
-from coremltools.converters.mil.mil.passes.quantization_passes import AbstractQuantizationPass, FP16ComputePrecision
-from coremltools.converters.mil.mil.passes.quantization_passes import ComputePrecision as precision
+from coremltools import (
+    ComputeUnit as _ComputeUnit,
+    __version__ as _ct_version
+)
+from coremltools.converters.mil.mil.passes.quantization_passes import (
+    AbstractQuantizationPass,
+    ComputePrecision as precision,
+    FP16ComputePrecision
+)
 from coremltools.converters.mil.input_types import InputType, ClassifierConfig
 from coremltools.converters.mil.converter import mil_convert
 from coremltools.converters.mil.mil import Program
 from coremltools._deps import _HAS_TORCH, _HAS_TF_1, _HAS_TF_2
 from coremltools.converters._profile_utils import _profile
 
 from coremltools.models import _METADATA_VERSION, _METADATA_SOURCE
+from coremltools.models.utils import _MLPACKAGE_EXTENSION
 from coremltools.converters.mil._deployment_compatibility import (
     AvailableTarget,
     check_deployment_compatibility,
@@ -250,10 +259,10 @@ def convert(
         - if False, identical to setting compute_units to `coremltools.ComputeUnit.ALL``
 
     package_dir : str
-        Post conversion, the model is compiled to form the MLModel object ready for prediction.
-        This requires a temporary directory to hold the mlmodelc archive.
-        - if not None, must be a path to a directory that is used for
-          temporarily storing the compiled model assets. If None, a temporary directory is created.
+        Post conversion, the model is saved at a temporary location and
+        loaded to form the MLModel object ready for prediction.
+        If package_dir is provided, model will be saved at this location instead of creating a temporary directory.
+        - if not None, must be a path to a directory with extension .mlpackage
 
     debug : bool
         This flag should generally be False except for debugging purposes
@@ -312,8 +321,6 @@ def convert(
     See `neural-network-conversion <https://coremltools.readme.io/docs/neural-network-conversion>`_ for
     more advanced options.
     """
-    from coremltools import __version__ as ct_version
-
     _check_deployment_target(minimum_deployment_target)
     exact_source = _determine_source(model, source, outputs)
     exact_target = _determine_target(convert_to, minimum_deployment_target)
@@ -337,6 +344,11 @@ def convert(
     else:
         raise ValueError("Invalid value of the argument 'compute_precision'")
 
+    if package_dir is not None:
+        _, ext = os.path.splitext(package_dir)
+        if ext != _MLPACKAGE_EXTENSION:
+            raise Exception("If package_dir is provided, it must have extension {} (not {})".format(_MLPACKAGE_EXTENSION, ext))
+
     mlmodel = mil_convert(
         model,
         convert_from=exact_source,
@@ -363,8 +375,7 @@ def convert(
 
     gc.collect()
 
-    mlmodel = _record_src_version(mlmodel, exact_source)
-    mlmodel.user_defined_metadata[_METADATA_VERSION] = ct_version
+    mlmodel = _record_build_metadata(mlmodel, exact_source)
 
     return mlmodel
 
@@ -553,16 +564,27 @@ def _determine_target(convert_to, minimum_deployment_target):
         else:
             return "mlprogram"
 
-def _record_src_version(mlmodel, exact_source):
+def _record_build_metadata(mlmodel, exact_source):
     # recording metadata: coremltools version, source framework and version
+    src_pkg, pkg_ver = None, None
     if exact_source in {"tensorflow", "tensorflow2"} and (_HAS_TF_1 or _HAS_TF_2):
+        src_pkg, pkg_ver = "tensorflow", tf.__version__
         src_pkg_version = "tensorflow=={0}".format(tf.__version__)
     elif exact_source == "pytorch" and _HAS_TORCH:
+        src_pkg, pkg_ver = "pytorch", torch.__version__
         src_pkg_version = "torch=={0}".format(torch.__version__)
     elif exact_source == 'milinternal':
+        src_pkg, pkg_ver = "milinternal", ""
         src_pkg_version = "milinternal"
     else:
         raise ValueError('Unsupported source {}'.format(exact_source))
 
     mlmodel.user_defined_metadata[_METADATA_SOURCE] = src_pkg_version
+    mlmodel.user_defined_metadata[_METADATA_VERSION] = _ct_version
+
+    build_info = {'coremltools-version': _ct_version}
+    if src_pkg is not None and pkg_ver is not None:
+        build_info['coremltools-component-' + src_pkg] = str(pkg_ver)
+    mlmodel._set_build_info_mil_attributes(build_info)
+
     return mlmodel

coremltools/converters/keras/_topology2.py

@@ -1,3 +1,8 @@
+# Copyright (c) 2021, Apple Inc. All rights reserved.
+#
+# Use of this source code is governed by a BSD-3-clause license that can be
+# found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
+
 import keras as _keras
 import numpy as _np
 

coremltools/converters/mil/_deployment_compatibility.py

@@ -1,6 +1,15 @@
+# Copyright (c) 2021, Apple Inc. All rights reserved.
+#
+# Use of this source code is governed by a BSD-3-clause license that can be
+# found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
+
 from enum import Enum
-from coremltools import _SPECIFICATION_VERSION_IOS_13, _SPECIFICATION_VERSION_IOS_14, \
-                        _SPECIFICATION_VERSION_IOS_15
+
+from coremltools import (
+    _SPECIFICATION_VERSION_IOS_13,
+    _SPECIFICATION_VERSION_IOS_14,
+    _SPECIFICATION_VERSION_IOS_15
+)
 
 
 class AvailableTarget(Enum):
@@ -15,7 +24,7 @@ class AvailableTarget(Enum):
     macOS10_15 = _SPECIFICATION_VERSION_IOS_13
     macOS10_16 = _SPECIFICATION_VERSION_IOS_14
     macOS11 = _SPECIFICATION_VERSION_IOS_14
-    macOS12 = _SPECIFICATION_VERSION_IOS_14
+    macOS12 = _SPECIFICATION_VERSION_IOS_15
 
     # watchOS versions (aliases of iOS versions)
     watchOS6 = _SPECIFICATION_VERSION_IOS_13

coremltools/converters/mil/backend/mil/helper.py

@@ -3,16 +3,15 @@
 #  Use of this source code is governed by a BSD-3-clause license that can be
 #  found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
 
-import numpy as np
 import os
-import re
+
+import numpy as np
 
 from coremltools.converters.mil.mil import types
 from coremltools.converters.mil.mil.types import builtin_to_proto_types
-from coremltools.models.model import _WEIGHTS_DIR_NAME, _WEIGHTS_FILE_NAME
+from coremltools.models.utils import _WEIGHTS_DIR_NAME, _WEIGHTS_FILE_NAME
 import coremltools.proto.FeatureTypes_pb2 as ft
 import coremltools.proto.MIL_pb2 as pm
-
 from coremltools.converters.mil.mil.types import (
     type_to_builtin_type,
     numpy_type_to_builtin_type,
@@ -318,4 +317,3 @@ def cast_to_framework_io_dtype(var, is_output):
         ioname2 = "outputs" if is_output else "inputs"
         raise NotImplementedError(ioname + var.name + " has data type " + builtin_to_string(var.dtype) + \
                                   ". ML Program models only support fp32 and int32 " + ioname2 + ".")
-

coremltools/converters/mil/backend/mil/load.py

@@ -32,9 +32,8 @@
     any_variadic,
     is_symbolic,
 )
-from coremltools.converters.mil.mil.types.type_mapping import types_int64
 from coremltools.libmilstoragepython import _BlobStorageWriter as BlobWriter
-from coremltools.models.model import _WEIGHTS_FILE_NAME
+from coremltools.models.utils import _WEIGHTS_FILE_NAME
 from coremltools.models.neural_network.flexible_shape_utils import (
     add_enumerated_image_sizes,
     add_multiarray_ndshape_enumeration,
@@ -105,8 +104,7 @@ def translate_generic_op(op, parameters, blob_writer, literal_params=[]):
     ]
     blocks = None
     if len(op.blocks) > 0:
-        blocks = [create_block(b, parameters, blob_writer) \
-                  for b in op.blocks]
+        blocks = [create_block(b, parameters, blob_writer) for b in op.blocks]
 
     op_type = op.op_type
     attr_dict = {}
@@ -253,8 +251,7 @@ def load(prog, weights_dir, resume_on_errors=False, **kwargs):
 
     function_protos = {}
     for func_name, func in prog.functions.items():
-        function_protos[func_name] = convert_function(func, prog.parameters,
-            blob_writer)
+        function_protos[func_name] = convert_function(func, prog.parameters, blob_writer)
 
     proto = pm.Program(
         version=1,

coremltools/converters/mil/backend/mil/passes/adjust_io_to_supported_types.py

@@ -1,16 +1,14 @@
-# -*- coding: utf-8 -*-
-
 #  Copyright (c) 2021, Apple Inc. All rights reserved.
 #
 #  Use of this source code is governed by a BSD-3-clause license that can be
 #  found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
 
-from coremltools.converters.mil.mil import Builder as mb
-from coremltools.converters.mil.mil import types as types
+import warnings as _warnings
+
+from coremltools.converters.mil.mil import Builder as mb, types as types
 from coremltools.converters.mil.mil.passes.pass_registry import register_pass
 from coremltools.converters.mil.mil.passes.graph_pass import AbstractGraphPass
 
-import warnings as _warnings
 
 @register_pass(namespace="mil_backend")
 class adjust_io_to_supported_types(AbstractGraphPass):
@@ -164,13 +162,11 @@ def _adjust_var(var):
 
 def _adjust_func_inputs(func):
     for input_name, input_var in func.inputs.items():
-       _adjust_var(input_var)
-
+        _adjust_var(input_var)
 
 def _adjust_block_inputs(block):
     for input_var in block.inputs:
-       _adjust_var(input_var)
-
+        _adjust_var(input_var)
 
 def _adjust_ops(block):
     len_block = len(block.operations)

coremltools/converters/mil/backend/mil/passes/insert_image_preprocessing_op.py

@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 #  Copyright (c) 2020, Apple Inc. All rights reserved.
 #
 #  Use of this source code is governed by a BSD-3-clause license that can be

coremltools/converters/mil/backend/mil/passes/sanitize_name_strings.py

@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 #  Copyright (c) 2020, Apple Inc. All rights reserved.
 #
 #  Use of this source code is governed by a BSD-3-clause license that can be
@@ -17,10 +15,7 @@ class sanitize_name_strings(AbstractGraphPass):
     of the format [a-zA-Z_][a-zA-Z0-9_]*
     """
     def apply(self, prog):
-	    sanitizer_vars = NameSanitizer(prefix="var_")
-	    sanitizer_ops = NameSanitizer(prefix="op_")
-
-	    for f in prog.functions.values():
-	        sanitize_block(f, sanitizer_vars, sanitizer_ops, prog.main_input_types)
-
-
+        for f in prog.functions.values():
+            sanitizer_vars = NameSanitizer(prefix="var_")
+            sanitizer_ops = NameSanitizer(prefix="op_")
+            sanitize_block(f, sanitizer_vars, sanitizer_ops, prog.main_input_types)