[Py] Add convolution_backprop_data to API (#1292)

* [Py] Add convolution_backprop_data to API

* Conv fix

python/ngraph/__init__.py

@@ -31,6 +31,7 @@ from ngraph.ops import concat
 from ngraph.ops import constant
 from ngraph.ops import convert
 from ngraph.ops import convolution
+from ngraph.ops import convolution_backprop_data
 from ngraph.ops import cos
 from ngraph.ops import cosh
 from ngraph.ops import divide

python/ngraph/ops.py

@@ -21,11 +21,11 @@ from ngraph.impl import AxisSet, AxisVector, Coordinate, CoordinateDiff, Functio
     NodeVector, Shape, Strides
 
 from ngraph.impl.op import Abs, Acos, Add, And, Asin, Atan, AvgPool, BatchNorm, Broadcast, \
-    Ceiling, Concat, Constant, Convert, Convolution, Cos, Cosh, Divide, Dot, Equal, Exp, Floor, \
-    FunctionCall, GetOutputElement, Greater, GreaterEq, Less, LessEq, Log, Max, Maximum, MaxPool, \
-    Min, Minimum, Multiply, Negative, Not, NotEqual, OneHot, Or, Pad, Parameter, Product, Power, \
-    Reduce, Relu, ReplaceSlice, Reshape, Reverse, Select, Sign, Sin, Sinh, Slice, Softmax, Sqrt, \
-    Subtract, Sum, Tan, Tanh
+    Ceiling, Concat, Constant, Convert, Convolution, ConvolutionBackpropData, Cos, Cosh, Divide, \
+    Dot, Equal, Exp, Floor, FunctionCall, GetOutputElement, Greater, GreaterEq, Less, LessEq, \
+    Log, Max, Maximum, MaxPool, Min, Minimum, Multiply, Negative, Not, NotEqual, OneHot, Or, Pad, \
+    Parameter, Product, Power, Reduce, Relu, ReplaceSlice, Reshape, Reverse, Select, Sign, Sin, \
+    Sinh, Slice, Softmax, Sqrt, Subtract, Sum, Tan, Tanh
 
 from typing import Callable, Iterable, List, Union
 
@@ -600,6 +600,49 @@ def convolution(data_batch,                     # type: Node
                        CoordinateDiff(padding_above), Strides(data_dilation_strides))
 
 
+@nameable_op
+def convolution_backprop_data(data_batch_shape,                      # type: TensorShape
+                              filters,                               # type: Node
+                              output_delta,                          # type: Node
+                              window_movement_strides_forward=None,  # type: List[int]
+                              window_dilation_strides_forward=None,  # type: List[int]
+                              padding_below_forward=None,            # type: List[int]
+                              padding_above_forward=None,            # type: List[int]
+                              data_dilation_strides_forward=None,    # type: List[int]
+                              name=None,                             # type: str
+                              ):
+    # type: (...) -> Node
+    """Return node performing a batched-convolution data batch-backprop operation.
+
+    :param data_batch_shape: The shape of the data batch from forward-prop.
+    :param filters: The node producing the filters from forward-prop.
+    :param output_delta: The node producing output delta.
+    :param window_movement_strides_forward: The window movement strides from forward-prop.
+    :param window_dilation_strides_forward: The window dilation strides from forward-prop.
+    :param padding_below_forward: The padding-below sizes from forward-prop.
+    :param padding_above_forward: The padding-above sizes from forward-prop.
+    :param data_dilation_strides_forward: The data dilation strides from forward-prop.
+    """
+    spatial_dim_count = len(data_batch_shape) - 2
+    if window_movement_strides_forward is None:
+        window_movement_strides_forward = [1] * spatial_dim_count
+    if window_dilation_strides_forward is None:
+        window_dilation_strides_forward = [1] * spatial_dim_count
+    if padding_below_forward is None:
+        padding_below_forward = [0] * spatial_dim_count
+    if padding_above_forward is None:
+        padding_above_forward = [0] * spatial_dim_count
+    if data_dilation_strides_forward is None:
+        data_dilation_strides_forward = [1] * spatial_dim_count
+
+    return ConvolutionBackpropData(Shape(data_batch_shape), filters, output_delta,
+                                   Strides(window_movement_strides_forward),
+                                   Strides(window_dilation_strides_forward),
+                                   CoordinateDiff(padding_below_forward),
+                                   CoordinateDiff(padding_above_forward),
+                                   Strides(data_dilation_strides_forward))
+
+
 @nameable_op
 def avg_pool(data_batch,             # type: Node
              window_shape,           # type: TensorShape

python/test/ngraph/test_convolution.py

@@ -93,3 +93,45 @@ def test_convolution_2d():
                                    [0, 0, 20, 20, 0],
                                    [0, 0, 20, 20, 0]]]],
                                 dtype=np.float32))
+
+
+@pytest.config.gpu_skip(reason='Not implemented')
+def test_convolution_backprop_data():
+    runtime = get_runtime()
+
+    data_batch_shape = [1, 1, 9, 9]
+    filter_shape = [1, 1, 3, 3]
+    output_delta_shape = [1, 1, 7, 7]
+
+    filter_param = ng.parameter(shape=filter_shape)
+    output_delta_param = ng.parameter(shape=output_delta_shape)
+
+    deconvolution = ng.convolution_backprop_data(data_batch_shape, filter_param, output_delta_param)
+
+    data_batch_data = np.array([[[[-20, -20, 20, 20, 0, 0, 0],
+                                  [-20, -20, 20, 20, 0, 0, 0],
+                                  [-20, -20, 20, 20, 0, 0, 0],
+                                  [-20, -20, 20, 20, 0, 0, 0],
+                                  [-20, -20, 20, 20, 0, 0, 0],
+                                  [-20, -20, 20, 20, 0, 0, 0],
+                                  [-20, -20, 20, 20, 0, 0, 0]]]],
+                               dtype=np.float32)
+
+    filter_data = np.array([
+        [1., 0., -1.],
+        [2., 0., -2.],
+        [1., 0., -1.]], dtype=np.float32).reshape(1, 1, 3, 3)
+
+    model = runtime.computation(deconvolution, filter_param, output_delta_param)
+    result = model(filter_data, data_batch_data)
+    assert np.allclose(result,
+                       np.array([[[[-20., -20., 40., 40., -20., -20., 0., 0., 0.],
+                                   [-60., -60., 120., 120., -60., -60., 0., 0., 0.],
+                                   [-80., -80., 160., 160., -80., -80., 0., 0., 0.],
+                                   [-80., -80., 160., 160., -80., -80., 0., 0., 0.],
+                                   [-80., -80., 160., 160., -80., -80., 0., 0., 0.],
+                                   [-80., -80., 160., 160., -80., -80., 0., 0., 0.],
+                                   [-80., -80., 160., 160., -80., -80., 0., 0., 0.],
+                                   [-60., -60., 120., 120., -60., -60., 0., 0., 0.],
+                                   [-20., -20., 40., 40., -20., -20., 0., 0., 0.]]]],
+                                dtype=np.float32))