[WIP] [Relay] [NNVM] [Frontend] implement MaxPool-8 and MaxPool-10 (#3114)

nnvm/python/nnvm/frontend/onnx.py

@@ -27,6 +27,13 @@ from .onnx_caffe2_utils import dimension_picker, dimension_constraint, \
 __all__ = ['from_onnx']
 
 
+def onnx_storage_order2layout(storage_order):
+    if storage_order not in (0, 1):
+        raise tvm.error.OpAttributeInvalid('Mode of storage_order must be either 0 or 1')
+
+    return 'NCHW' if sotrage_order == 0 else 'NHWC'
+
+
 class OnnxOpConverter(object):
     """ A helper class for holding onnx op converters.
     """
@@ -207,8 +214,38 @@ class Gemm(OnnxOpConverter):
 
 
 class MaxPool(Pool):
+    """ Operator converter for MaxPool
+    """
     name = 'max_pool'
 
+    @classmethod
+    def _impl_v8(cls, inputs, attr, params):
+        return AttrCvt(
+            op_name=dimension_picker(cls.name),
+            transforms={
+                'kernel_shape': 'pool_size',
+                'pads': ('padding', (0, 0), revert_caffe2_pad),
+                'storage_order': ('layout', 'NCHW', onnx_storage_order2layout),
+            },
+            # very weird attributes here in onnx, force check
+            ignores=['dilations', 'auto_pad'],
+            # TODO(higumachan): make sure ceil_mode in onnx, and layout?
+            extras={'ceil_mode': False},
+            custom_check=dimension_constraint())(inputs, attr, params)
+
+    @classmethod
+    def _impl_v10(cls, inputs, attr, params):
+        return AttrCvt(
+            op_name=dimension_picker(cls.name),
+            transforms={
+                'kernel_shape': 'pool_size',
+                'pads': ('padding', (0, 0), revert_caffe2_pad),
+                'storage_order': ('layout', 'NCHW', onnx_storage_order2layout),
+                'ceil_mode': 'ceil_mode'
+            },
+            # very weird attributes here in onnx, force check
+            ignores=['dilations', 'auto_pad'],
+            custom_check=dimension_constraint())(inputs, attr, params)
 
 class Mul(Elemwise):
     name = 'mul'

python/tvm/relay/frontend/onnx.py

@@ -52,6 +52,15 @@ def revert_caffe2_pad(pads):
             'Number of pads must be either 2 or 4.')
     return pads
 
+
+def onnx_storage_order2layout(storage_order):
+    """converter of onnx storage order parameter to tvm storage order format"""
+    if storage_order not in (0, 1):
+        raise tvm.error.OpAttributeInvalid('Mode of storage_order must be either 0 or 1')
+
+    return 'NCHW' if sotrage_order == 0 else 'NHWC'
+
+
 def dimension_constraint():
     def _dim_check(attrs):
         if len(attrs['kernel_shape']) == 2:
@@ -60,6 +69,7 @@ def dimension_constraint():
 
     return _dim_check, "Only 2d kernel supported."
 
+
 class OnnxOpConverter(object):
     """ A helper class for holding onnx op converters.
     """
@@ -108,6 +118,7 @@ class Elemwise(OnnxOpConverter):
             inputs[1] = _op.expand_dims(inputs[1], axis=axis, num_newaxis=2)
         return get_relay_op(op_name)(*inputs)
 
+
 class Pool(OnnxOpConverter):
     """ A helper class for pool op converters.
     """
@@ -247,6 +258,7 @@ class Gemm(OnnxOpConverter):
                            inputs[1], units=channels)
         return _op.nn.bias_add(out, _expr.const(beta) * inputs[2])
 
+
 class MatMul(OnnxOpConverter):
     """ Operator converter for MatMul.
     """
@@ -257,9 +269,40 @@ class MatMul(OnnxOpConverter):
         input_1_t = _op.transpose(inputs[1], axes=(1, 0))
         return _op.nn.dense(inputs[0], input_1_t)
 
+
 class MaxPool(Pool):
+    """ Operator converter for MaxPool
+    """
     name = 'max_pool'
 
+    @classmethod
+    def _impl_v8(cls, inputs, attr, params):
+        return AttrCvt(
+            op_name=dimension_picker(cls.name),
+            transforms={
+                'kernel_shape': 'pool_size',
+                'pads': ('padding', (0, 0), revert_caffe2_pad),
+                'storage_order': ('layout', 'NCHW', onnx_storage_order2layout),
+            },
+            # very weird attributes here in onnx, force check
+            ignores=['dilations', 'auto_pad'],
+            # TODO(higumachan): make sure ceil_mode in onnx, and layout?
+            extras={'ceil_mode': False},
+            custom_check=dimension_constraint())(inputs, attr, params)
+
+    @classmethod
+    def _impl_v10(cls, inputs, attr, params):
+        return AttrCvt(
+            op_name=dimension_picker(cls.name),
+            transforms={
+                'kernel_shape': 'pool_size',
+                'pads': ('padding', (0, 0), revert_caffe2_pad),
+                'storage_order': ('layout', 'NCHW', onnx_storage_order2layout),
+                'ceil_mode': 'ceil_mode'
+            },
+            # very weird attributes here in onnx, force check
+            ignores=['dilations', 'auto_pad'],
+            custom_check=dimension_constraint())(inputs, attr, params)
 
 class Mul(Elemwise):
     name = 'multiply'