Merge pull request pfnet-research#158 from durswd/const_separate

Const separate

elichika/elichika/functions_buildin.py

@@ -0,0 +1,64 @@
+import chainer
+import chainer.functions as F
+import chainer.links as L
+
+import onnx
+import onnx.helper as oh
+from onnx import numpy_helper
+from onnx import TensorProto
+from onnx import ModelProto
+
+import elichika.parser.core as core
+import elichika.parser.graphs as graphs
+import elichika.parser.values as values
+import elichika.parser.nodes as nodes
+import elichika.parser.functions as functions
+import elichika.parser.functions_builtin as functions_builtin
+import elichika.parser.values_builtin as values_builtin
+import elichika.parser.utils as utils
+
+import numpy as np
+import collections
+
+import elichika.onnx_converters as oc
+
+def convert_relu(onnx_graph, node):
+    onnx_graph.add_node('Relu', 
+        [node.inputs[0]],
+        [node.outputs[0]],
+        name = str(node.lineprop))
+
+def convert_softmax(onnx_graph, node):
+    onnx_graph.add_node(
+        "Softmax",
+        [node.inputs[0]],
+        [node.outputs[0]],
+        str(node.lineprop),
+        axis = oc.try_get_attribute(node.inputs[1]))
+
+def convert_pad_sequence(onnx_graph, node):
+    kwargs = {}
+
+    if node.inputs[1] is not None:
+        value = oc.try_get_attribute(node.inputs[1])
+        if value is not None:
+            kwargs['length'] = value
+        if node.inputs[2] is not None:
+            value = oc.try_get_attribute(node.inputs[2])
+            if value != 0:
+                kwargs['value'] = float(value)
+
+    onnx_graph.add_node(
+        "ChainerSequencePad",
+        [node.inputs[0]],
+        [node.outputs[0]],
+        str(node.lineprop),
+        **kwargs)
+
+def convert_softmax_cross_entropy(onnx_graph, node):
+
+    onnx_graph.add_node(
+        "ChainerSoftmaxCrossEntropy",
+        node.inputs,
+        node.outputs,
+        str(node.lineprop))

elichika/elichika/layers_buikdin.py

@@ -0,0 +1,119 @@
+import chainer
+import chainer.functions as F
+import chainer.links as L
+
+import onnx
+import onnx.helper as oh
+from onnx import numpy_helper
+from onnx import TensorProto
+from onnx import ModelProto
+
+import elichika.parser.core as core
+import elichika.parser.graphs as graphs
+import elichika.parser.values as values
+import elichika.parser.nodes as nodes
+import elichika.parser.functions as functions
+import elichika.parser.functions_builtin as functions_builtin
+import elichika.parser.values_builtin as values_builtin
+import elichika.parser.utils as utils
+
+import numpy as np
+import collections
+
+import elichika.onnx_converters as oc
+
+def convert_onnx_chainer_linear(onnx_graph : 'ONNXGraph', node : 'nodes.Node'):
+    chainer_inst = node.func.owner.inst # type: chainer.links.Linear
+    onnx_name = oc.node2onnx_parameter[node].onnx_name
+
+    x = oc.ONNXValue(onnx_graph, node.inputs[0])
+    o = oc.ONNXValue(onnx_graph, node.outputs[0])
+
+    if chainer_inst.W.data is None:
+        print("W is unknown. Please infer this model.")
+
+    w = oc.ONNXValue(onnx_graph, chainer_inst.W)
+
+    (x_shape,) = onnx_graph.add_node(
+        'Shape',
+        [x],
+        [None],
+        str(node.lineprop))
+
+    (batch_size_1,) = onnx_graph.add_node(
+        'Gather',
+        [x_shape, oc.ONNXValue(onnx_graph, np.array(0, dtype=np.int64), [onnx_name, '/Zero'])],
+        [None],
+        str(node.lineprop))
+
+    (batch_size_2,) = onnx_graph.add_node(
+        'Unsqueeze',
+        [batch_size_1],
+        [None],
+        str(node.lineprop),
+        axes=[0])
+
+    (mat_shape,) = onnx_graph.add_node(
+        'Concat',
+        [batch_size_2, oc.ONNXValue(onnx_graph, np.array([-1], dtype=np.int64), [onnx_name, '/Minus1'])],
+        [None],
+        str(node.lineprop),
+        axis=0)
+
+    (x_reshape,) = onnx_graph.add_node(
+        'Reshape',
+        [x, mat_shape],
+        [None],
+        str(node.lineprop))
+
+    if chainer_inst.b is not None:
+        b = oc.ONNXValue(onnx_graph, chainer_inst.b)
+
+        onnx_graph.add_node(
+            'Gemm',
+            [x_reshape, w, b],
+            [o],
+            str(node.lineprop),
+            transA=0,
+            transB=1)
+    else:
+        temp = oc.ONNXValue(onnx_graph, np.float32, [onnx_name, '/Temp'])
+
+        onnx_graph.add_node(
+            'Transpose',
+            [w],
+            [temp],
+            str(node.lineprop),
+            perm=[1, 0])
+
+        onnx_graph.add_node(
+            'MatMul',
+            [x_reshape, temp],
+            [o],
+            str(node.lineprop))
+
+def convert_onnx_chainer_convolution2d(onnx_graph : 'ONNXGraph', node : 'nodes.Node'):
+    chainer_inst = node.func.owner.inst # type: chainer.links.Convolution2D
+    onnx_name = oc.node2onnx_parameter[node].onnx_name
+
+    ksize = oc.size2d(chainer_inst.ksize)
+    stride = oc.size2d(chainer_inst.stride)
+    ps = oc.size2d(chainer_inst.pad)
+    pads = ps + ps
+
+    x = oc.ONNXValue(onnx_graph, node.inputs[0])
+    o = oc.ONNXValue(onnx_graph, node.outputs[0])
+    w = oc.ONNXValue(onnx_graph, chainer_inst.W)
+    b = None
+
+    if chainer_inst.b is not None:
+        b = oc.ONNXValue(onnx_graph, chainer_inst.b)
+
+    onnx_graph.add_node(
+        'Conv',
+        [x, w] + ([] if b is None else [b]),
+        [o],
+        str(node.lineprop),
+        kernel_shape=ksize,
+        pads=pads,
+        strides=stride)