Merge pull request #248 from disktnk/fix/backport-chainer-v7rc

Backport from Chainer v7rc

.travis.yml

@@ -11,6 +11,10 @@ matrix:
         - ONNX_VER="1.4.1"
     - name: "Py36, Chainer v6"
       python: "3.6"
+    - name: "Py37, Chainer v6, ONNX 1.5.0"
+      python: "3.7"
+      env:
+        - ONNX_VER="1.5.0"
     - name: "Py37, Chainer v6"
       python: "3.7"
       env:

README.md

@@ -9,13 +9,7 @@ This is an add-on package for ONNX support by Chainer.
 
 ## Tested environment
 
-- Python 3.5.5, 3.6.7, 3.7.2
-- ONNX 1.4.1, 1.5.0
-    - opset version 7, 8, 9, 10
-- Chainer stable, preview
-- ONNX-Runtime 0.4.0
-
-**(You can still specify all opset versions <= 9, but please noted that opset versions <= 6 are not tested)**
+see [Tested environments](https://onnx-chainer.readthedocs.io/en/latest/introduction/index.html#tested-environments)
 
 ## Installation
 
@@ -73,137 +67,11 @@ onnx_chainer.export(model, x, filename='vgg16.onnx')
 
 ## Supported Functions
 
-Currently 84 Chainer Functions are supported to export in ONNX format.
-
-### Activation
-
-- ClippedReLU
-- ELU
-- HardSigmoid
-- LeakyReLU
-- LogSoftmax
-- PReLUFunction
-- ReLU
-- Selu
-- Sigmoid
-- Softmax
-- Softplus
-- Tanh
-
-### Array
-
-- Cast
-- Concat
-- Copy
-- Depth2Space
-- Dstack
-- ExpandDims
-- GetItem
-- Hstack
-- Pad <sup>[1](#pad1)</sup><sup>[2](#pad2)</sup>
-- Repeat
-- Reshape
-- ResizeImages
-- Separate
-- Shape <sup>[5](#shape1)</sup>
-- Space2Depth
-- SplitAxis
-- Squeeze
-- Stack
-- Swapaxes
-- Tile
-- Transpose
-- Vstack
-- Where
-
-### Connection
-
-- Convolution2DFunction
-- ConvolutionND
-- Deconvolution2DFunction
-- DeconvolutionND
-- EmbedIDFunction <sup>[3](#embed1)</sup>
-- LinearFunction
-
-### Loss
-
-- SoftmaxCrossEntropy
-
-### Math
-
-- Absolute
-- Add
-- AddConstant
-- ArcCos
-- ArcSin
-- ArcTan
-- ArgMax
-- ArgMin
-- BroadcastTo
-- Cos
-- Cosh
-- Clip
-- Div
-- DivFromConstant
-- Exp
-- Identity
-- LinearInterpolate
-- Log
-- LogSumExp
-- MatMul
-- Max
-- Maximum
-- Mean
-- Min
-- Minimum
-- Mul
-- MulConstant
-- Neg
-- PowConstVar
-- PowVarConst
-- PowVarVar
-- Prod
-- RsqrtGPU
-- Sin
-- Sinh
-- Sqrt
-- Square
-- Sub
-- SubFromConstant
-- Sum
-- Tan
-
-### Noise
-
-- Dropout <sup>[4](#dropout1)</sup>
-
-### Normalization
-
-- BatchNormalization
-- FixedBatchNormalization
-- LocalResponseNormalization
-- NormalizeL2
-
-### Pooling
-
-- AveragePooling2D
-- AveragePoolingND
-- MaxPooling2D
-- MaxPoolingND
-- ROIPooling2D
-- Unpooling2D
+see [Supported Functions](https://onnx-chainer.readthedocs.io/en/latest/introduction/index.html#supported-functions)
 
 
 ## Contribution
 
 Any contribution to ONNX-Chainer is welcome!
 
 - Python codes follow [Chainer Coding Guidelines](https://docs.chainer.org/en/stable/contribution.html#coding-guidelines)
-
----
-
-<a name="pad1">1</a>: mode should be either 'constant', 'reflect', or 'edge'<br />
-<a name="pad2">2</a>: ONNX doesn't support multiple constant values for Pad operation<br />
-<a name="embed1">3</a>: Current ONNX doesn't support ignore_label for EmbedID<br />
-<a name="dropout1">4</a>: In test mode, all dropout layers aren't included in the exported file<br />
-<a name="shape1">5</a>: Chainer doesn't support Shape function<br />

docs/source/introduction/index.rst

@@ -41,6 +41,8 @@ First, install `ChainerCV <https://github.com/chainer/chainercv>`__ to get the p
 
 ``vgg16.onnx`` file will be exported.
 
+Other export examples are put on `examples <https://github.com/chainer/onnx-chainer/tree/master/examples>`__. Please check them.
+
 Supported Functions
 -------------------
 
@@ -71,6 +73,7 @@ Currently 82 Chainer Functions are supported to export in ONNX format.
 * GetItem
 * Hstack
 * Pad [#pad1]_ [#pad2]_
+* Permutate
 * Repeat
 * Reshape
 * ResizeImages
@@ -172,12 +175,12 @@ Tested Environments
     * Windows 10
 
 * Python 3.5.5, 3.6.7, 3.7.2
-* ONNX 1.4.1, 1.5.0
+* ONNX 1.4.1, 1.5.0, 1.6.0
 
-    * opset version 7, 8, 9, 10
+    * opset version 7, 8, 9, 10, 11
 
-* Chainer stable, preview
-* ONNX-Runtime 0.4.0
+* Chainer 6.5.0
+* ONNX-Runtime 1.0.0
 
 
 Run Test
@@ -211,6 +214,6 @@ on GPU environment::
 Contribution
 ------------
 
-Any contribution to ONNX-Chainer is welcom!
+Any contribution to ONNX-Chainer is welcome!
 
-* Python codes follow `Chainer Coding Guidelines <ttps://docs.chainer.org/en/stable/contribution.html#coding-guidelines>`__
+* Python codes follow `Chainer Coding Guidelines <https://docs.chainer.org/en/stable/contribution.html#coding-guidelines>`__

onnx_chainer/export.py

@@ -4,34 +4,34 @@
 import warnings
 
 import chainer
-import onnx
-from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE
-from onnx import shape_inference
-
-from onnx_chainer.context import Context
-from onnx_chainer.graph import Graph
-from onnx_chainer import mapping
-from onnx_chainer.onnx_helper import is_support_non_standard_domain
 
 try:
+    import onnx
     from onnx import checker
     from onnx import helper
+    from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE
     from onnx import numpy_helper
+    from onnx import shape_inference
+
+    from onnx_chainer.context import Context
+    from onnx_chainer.graph import Graph
+    from onnx_chainer import mapping
+    from onnx_chainer.onnx_helper import is_support_non_standard_domain
 
     _available = True
 except ImportError:
     _available = False
 
 MINIMUM_OPSET_VERSION = 7
-MAXIMUM_OPSET_VERSION = 10
+MAXIMUM_OPSET_VERSION = 11
 
 
 def _check_available():
     if not _available:
         raise ImportError(
             'ONNX is not installed on your environment. Exporting your model '
             'in ONNX format needs the onnx package.\n\n'
-            '\t$ pip install onnx\n\n')
+            '\t$ pip install \'onnx<1.7.0\'\n\n')
 
 
 def convert_parameter(parameter, context):
@@ -462,7 +462,8 @@ def _export(model, args, filename, export_params, graph_name, save_text,
 
     if input_shapes is not None:
         for output in model.graph.output:
-            output.type.Clear()
+            for d in output.type.tensor_type.shape.dim:
+                d.Clear()
         model = shape_inference.infer_shapes(model)
         check_onnx_model(model, external_converters, external_opset_imports)
 

onnx_chainer/export_testcase.py

@@ -3,9 +3,13 @@
 
 import chainer
 
-from onnx_chainer.export import export
-from onnx_chainer.onnx_helper import cleanse_param_name
-from onnx_chainer.onnx_helper import write_tensor_pb
+from onnx_chainer.export import _available
+
+if _available:
+    from onnx_chainer.export import export
+
+    from onnx_chainer.onnx_helper import cleanse_param_name
+    from onnx_chainer.onnx_helper import write_tensor_pb
 
 
 def export_testcase(model, args, out_dir, output_grad=False, **kwargs):

onnx_chainer/functions/__init__.py

@@ -21,6 +21,7 @@
 from onnx_chainer.functions.array import convert_Hstack  # NOQA
 from onnx_chainer.functions.array import convert_Moveaxis  # NOQA
 from onnx_chainer.functions.array import convert_Pad  # NOQA
+from onnx_chainer.functions.array import convert_Permutate  # NOQA
 from onnx_chainer.functions.array import convert_Repeat  # NOQA
 from onnx_chainer.functions.array import convert_Reshape  # NOQA
 from onnx_chainer.functions.array import convert_ResizeImages  # NOQA

onnx_chainer/functions/activation.py

@@ -1,3 +1,5 @@
+import numpy as np
+
 from onnx_chainer.functions.opset_version import support
 from onnx_chainer import onnx_helper
 
@@ -25,7 +27,7 @@ def _convert_softmax_impl(op_type, func, input_names, output_names):
     return gb.nodes(output_names=output_names)
 
 
-@support((1, 6))
+@support((1, 6, 11))
 def convert_ClippedReLU(
         func, opset_version, input_names, output_names, context):
     if opset_version == 1:
@@ -39,6 +41,12 @@ def convert_ClippedReLU(
             'Clip', input_names, output_names,
             min=0.0, max=func.cap,
         ),
+    elif opset_version == 11:
+        min_name = context.add_const(np.array(0, dtype=np.float32), 'zero')
+        max_name = context.add_const(
+            np.array(func.cap, dtype=np.float32), 'clip_z')
+        input_names.extend([min_name, max_name])
+        return onnx_helper.make_node('Clip', input_names, output_names),
 
 
 @support((1, 6))