[KERAS]Global MaxPool3d and AvgPool3d support

python/tvm/relay/frontend/keras.py

@@ -373,7 +373,7 @@ def _convert_convolution3d(inexpr, keras_layer, etab):
         pad_d3 = _get_pad_pair(in_d3, dilated_kernel_d3, stride_d3)
         params['padding'] = [pad_d1[0], pad_d2[0], pad_d3[0], pad_d1[1], pad_d2[1], pad_d3[1]]
     else:
-        msg = 'Padding with {} is not supported for operator Convolution ' \
+        msg = 'Padding with {} is not supported for operator Convolution3D ' \
               'in frontend Keras.'
         raise tvm.error.OpAttributeUnImplemented(msg.format(keras_layer.padding))
     out = _op.nn.conv3d(data=inexpr, **params)
@@ -543,6 +543,23 @@ def _convert_pooling3d(inexpr, keras_layer, etab):
 
     return _op.transpose(out, axes=(0, 2, 3, 4, 1))
 
+
+def _convert_global_pooling3d(inexpr, keras_layer, etab):
+    _check_data_format(keras_layer)
+    pool_type = type(keras_layer).__name__
+
+    global_pool_params = {'layout': etab.data_layout}
+    if pool_type == 'GlobalMaxPooling3D':
+        out = _op.nn.global_max_pool3d(inexpr, **global_pool_params)
+    elif pool_type == 'GlobalAveragePooling3D':
+        out = _op.nn.global_avg_pool3d(inexpr, **global_pool_params)
+    else:
+        raise tvm.error.OpNotImplemented(
+            'Operator {} is not supported for frontend Keras.'.format(keras_layer))
+
+    return _convert_flatten(out, keras_layer, etab)
+
+
 def _convert_upsample(inexpr, keras_layer, etab):
     _check_data_format(keras_layer)
     upsample_type = type(keras_layer).__name__
@@ -885,8 +902,8 @@ def _default_skip(inexpr, keras_layer, _): # pylint: disable=unused-argument
     # 'SeparableConv3D'        : _convert_convolution3d,
     'MaxPooling3D'             : _convert_pooling3d,
     'AveragePooling3D'         : _convert_pooling3d,
-    # 'GlobalMaxPooling3D'     : _convert_pooling3d,
-    # 'GlobalAveragePooling3D' : _convert_pooling3d,
+    'GlobalMaxPooling3D'       : _convert_global_pooling3d,
+    'GlobalAveragePooling3D'   : _convert_global_pooling3d,
     'UpSampling3D'             : _convert_upsample3d,
     'ZeroPadding3D'            : _convert_padding3d,
 

python/tvm/relay/op/nn/nn.py

@@ -2692,3 +2692,59 @@ def adaptive_avg_pool3d(data,
     """
     output_size = [] or output_size
     return _make.adaptive_avg_pool3d(data, output_size, layout)
+
+
+def global_max_pool3d(data,
+                      layout="NCDHW"):
+    r"""3D global maximum pooling operator.
+    This operator takes data as input and does 3D max value calculation
+    across each window represented by DxWxH.
+    In the default case, where the data_layout is `NCDHW`
+    a data Tensor with shape `(batch_size, in_channels, depth, height, width)`,
+    to produce an output Tensor with the following rule:
+    with data of shape (b, c, d, h, w)
+    .. math::
+        \mbox{out}(b, c, 1, 1, 1)  =  \max_{l=0, \ldots, d},  \max_{m=0, \ldots, h},
+              \max_{n=0, \ldots, w} \mbox{data}(b, c, l, m, n)
+
+    Parameters
+    ----------
+    data : tvm.relay.Expr
+        The input data to the operator.
+    layout : str, optional
+        Layout of the input.
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The computed result.
+    """
+    output_size = [1, 1, 1]
+    return _make.adaptive_max_pool3d(data, output_size, layout)
+
+
+def global_avg_pool3d(data,
+                      layout="NCDHW"):
+    r"""3D global average pooling operator.
+    This operator takes data as input and does 3D average value calculation
+    across each window represented by DxWxH.
+    In the default case, where the data_layout is `NCDHW`
+    a data Tensor with shape `(batch_size, in_channels, depth, height, width)`,
+    to produce an output Tensor with the following rule:
+    with data of shape (b, c, d, h, w)
+    .. math::
+        \mbox{out}(b, c, 1, 1, 1)  = \frac{1}{d * h * w} \sum_{l=0}^{d-1}  \sum_{m=0}^{h-1}
+              \sum_{n=0}^{w-1} \mbox{data}(b, c, l, m, n)
+
+    Parameters
+    ----------
+    data : tvm.relay.Expr
+        The input data to the operator.
+    layout : str, optional
+        Layout of the input.
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The computed result.
+    """
+    output_size = [1, 1, 1]
+    return _make.adaptive_avg_pool3d(data, output_size, layout)

tests/python/frontend/keras/test_forward.py

@@ -483,6 +483,17 @@ def test_forward_embedding(self, keras):
         keras_model = keras.models.Model(data, x)
         verify_keras_frontend(keras_model, need_transpose=False)
 
+    def test_forward_global_pool3d(self, keras):
+        data = keras.layers.Input(shape=(32, 32, 32, 1))
+        pool_funcs = [# global maxpool
+                      keras.layers.GlobalMaxPooling3D(),
+                      # global avgpool
+                      keras.layers.GlobalAveragePooling3D()
+                     ]
+        for pool_func in pool_funcs:
+            x = pool_func(data)
+            keras_model = keras.models.Model(data, x)
+            verify_keras_frontend(keras_model, layout='NDHWC')
 
 if __name__ == '__main__':
     for k in [keras, tf_keras]:
@@ -513,6 +524,7 @@ def test_forward_embedding(self, keras):
         sut.test_forward_mobilenet(keras=k, layout='NHWC')
         sut.test_forward_conv3d(keras=k)
         sut.test_forward_pool3d(keras=k)
+        sut.test_forward_global_pool3d(keras=k)
         sut.test_forward_upsample3d(keras=k)
         sut.test_forward_zero_padding3d(keras=k)
         sut.test_forward_embedding(keras=k)