release 4.0b3 (#858)

Author: bhushan23

README.md

@@ -1,4 +1,4 @@
-[![Build Status](https://img.shields.io/gitlab/pipeline/zach_nation/coremltools/master)](#)
+[![Build Status](https://travis-ci.com/apple/coremltools.svg?branch=master)](#)
 [![PyPI Release](https://img.shields.io/pypi/v/coremltools.svg)](#)
 [![Python Versions](https://img.shields.io/pypi/pyversions/coremltools.svg)](#)
 
@@ -29,7 +29,7 @@ With coremltools, you can do the following:
 To get the latest version of coremltools:
 
 ```shell
-pip install coremltools==4.0b2
+pip install coremltools==4.0b3
 ```
 
 For the latest changes please see the [release notes](https://github.com/apple/coremltools/releases/).

coremltools/_deps/__init__.py

@@ -12,6 +12,7 @@
 import platform as _platform
 import re as _re
 import sys as _sys
+from packaging import version
 
 
 def __get_version(version):
@@ -104,15 +105,15 @@ def __get_sklearn_version(version):
 
     if _HAS_TF_1:
         if tf_ver < _StrictVersion(_TF_1_MIN_VERSION):
-            _logging.warn(
+            _logging.warning(
                 (
                     "TensorFlow version %s is not supported. Minimum required version: %s ."
                     "TensorFlow conversion will be disabled."
                 )
                 % (tensorflow.__version__, _TF_1_MIN_VERSION)
             )
         elif tf_ver > _StrictVersion(_TF_1_MAX_VERSION):
-            _logging.warn(
+            _logging.warning(
                 "TensorFlow version %s detected. Last version known to be fully compatible is %s ."
                 % (tensorflow.__version__, _TF_1_MAX_VERSION)
             )
@@ -126,7 +127,7 @@ def __get_sklearn_version(version):
                 % (tensorflow.__version__, _TF_2_MIN_VERSION)
             )
         elif tf_ver > _StrictVersion(_TF_2_MAX_VERSION):
-            _logging.warn(
+            _logging.warning(
                 "TensorFlow version %s detected. Last version known to be fully compatible is %s ."
                 % (tensorflow.__version__, _TF_2_MAX_VERSION)
             )
@@ -232,6 +233,7 @@ def __get_sklearn_version(version):
     _HAS_TORCH = False
 MSG_TORCH_NOT_FOUND = "PyTorch not found."
 
+
 # ---------------------------------------------------------------------------------------
 _HAS_ONNX = True
 try:
@@ -246,3 +248,17 @@ def __get_sklearn_version(version):
 except:
     _HAS_GRAPHVIZ = False
 MSG_ONNX_NOT_FOUND = "ONNX not found."
+
+# General utils
+def version_ge(module, target_version):
+    """
+    Example usage:
+
+    >>> import torch # v1.5.0
+    >>> version_ge(torch, '1.6.0') # False
+    """
+    return version.parse(module.__version__) >= version.parse(target_version)
+
+def version_lt(module, target_version):
+    """See version_ge"""
+    return version.parse(module.__version__) < version.parse(target_version)

coremltools/converters/_converters_entry.py

@@ -73,14 +73,21 @@ def convert(
         to determine the source framework.
 
     inputs: list of `TensorType` or `ImageType`
-        - Inputs are required for PyTorch model, but optional for TensorFlow.
-        - For PyTorch models, the inputs may be nested list or tuple, but for
-          TensorFlow models it must be a flat list.
-        - For TensorFlow, if inputs is `None`, the inputs are `Placeholder`
-          nodes in the model (if model is frozen graph) or function inputs (if
-          model is tf function).
-        - For TensorFlow, if inputs is not `None`, inputs may contain only a
-          subset of all Placeholder in the TF model.
+        TensorFlow 1 and 2:
+            - `inputs` are optional. If not provided, the inputs are
+              `Placeholder` nodes in the model (if model is frozen graph) or
+              function inputs (if model is tf function)
+            - `inputs` must corresponds to all or some of the Placeholder
+              nodes in the TF model
+            - `TensorType` and `ImageType` in `inputs` must have `name`
+              specified. `shape` is optional.
+            - If `inputs` is provided, it must be a flat list.
+
+        PyTorch:
+            - `inputs` are required.
+            - `inputs` may be nested list or tuple.
+            - `TensorType` and `ImageType` in `inputs` must have `name`
+              and `shape` specified.
 
     outputs: list[str] (optional)
 
@@ -263,6 +270,10 @@ def raise_if_duplicated(input_list):
             msg = 'Unexpected argument "example_inputs" found'
             raise ValueError(msg)
 
+        if inputs is None:
+            msg = 'Expected argument for pytorch "inputs" not provided'
+            raise ValueError(msg)
+
         def _flatten_list(_inputs):
             ret = []
             for _input in _inputs:
@@ -313,6 +324,9 @@ def _flatten_list(_inputs):
             **kwargs
         )
 
+    if convert_to == 'mil':
+        return proto_spec # Returns the MIL program
+
     model = coremltools.models.MLModel(proto_spec, useCPUOnly=True)
 
     if minimum_deployment_target is not None:

coremltools/converters/mil/_deployment_compatibility.py

@@ -93,6 +93,9 @@ def iOS14Features(spec):
         if layer_type == "sliceStatic" and layer.sliceDynamic.squeezeMasks:
             msg = "Squeeze mask for static slice operation"
 
+        if layer_type == "concatND" and layer.concatND.interleave:
+            msg = "Concat layer with interleave operation"
+
         if msg != "" and (msg not in features_list):
             features_list.append(msg)
 

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

@@ -11,6 +11,7 @@
 import logging
 from collections import defaultdict
 
+import coremltools as ct
 from coremltools.converters.mil.input_types import (
     ClassifierConfig,
     ImageType,
@@ -171,25 +172,25 @@ def _set_optional_inputs(proto, input_types):
     for input_type in input_types:
         if isinstance(input_type, ImageType):
             continue
-        is_optional = input_type.is_optional
-        optional_value = input_type.optional_value
-        shape = input_type.shape
-        if not is_optional:
-            continue
-        msg = "Not support optional inputs for flexible input shape."
-        if not isinstance(shape, Shape):
-            raise NotImplementedError(msg)
-        if any([isinstance(s, RangeDim) for s in shape.shape]):
-            raise NotImplementedError(msg)
-
-        default_map[input_type.name] = optional_value
+        if input_type.default_value is not None:
+            default_map[input_type.name] = input_type.default_value
 
     for idx, input in enumerate(proto.description.input):
         name = proto.description.input[idx].name
         if name in default_map:
+            default_value = default_map[name]
             proto.description.input[idx].type.isOptional = True
-            default_value = default_map[name] if default_map[name] is not None else 0.
-            proto.description.input[idx].type.multiArrayType.floatDefaultValue = default_value
+            array_t = proto.description.input[idx].type.multiArrayType
+            default_fill_val = default_value.flatten()[0]
+            array_t.floatDefaultValue = default_fill_val
+            if default_fill_val != 0 or list(default_value.shape) != \
+                array_t.shape:
+                # promote spec version to 5 and set the default value
+                proto.specificationVersion = max(proto.specificationVersion,
+                    ct._SPECIFICATION_VERSION_IOS_14)
+                # array_t.shape is not empty.
+                array_t.ClearField('shape')
+                array_t.shape.extend(list(default_value.shape))
 
 
 @_profile