add api image gradients

mindspore/nn/layer/__init__.py

@@ -22,7 +22,7 @@ from .normalization import BatchNorm1d, BatchNorm2d, LayerNorm
 from .container import SequentialCell, CellList
 from .conv import Conv2d, Conv2dTranspose
 from .lstm import LSTM
-from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot
+from .basic import Dropout, Flatten, Dense, ClipByNorm, Norm, OneHot, ImageGradients
 from .embedding import Embedding
 from .pooling import AvgPool2d, MaxPool2d
 
@@ -31,7 +31,7 @@ __all__ = ['Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid',
            'SequentialCell', 'CellList',
            'Conv2d', 'Conv2dTranspose',
            'LSTM',
-           'Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot',
+           'Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', 'ImageGradients',
            'Embedding',
            'AvgPool2d', 'MaxPool2d',
            ]

mindspore/nn/layer/basic.py

@@ -370,3 +370,48 @@ class OneHot(Cell):
 
     def construct(self, indices):
         return self.onehot(indices, self.depth, self.on_value, self.off_value)
+
+
+class ImageGradients(Cell):
+    r"""
+    Returns two tensors, the first is along the height dimension and the second is along the width dimension.
+
+    Assume an image shape is :math:`h*w`. The gradients along the height and the width are :math:`dy` and :math:`dx`,
+    respectively.
+
+    .. math::
+        dy[i] = \begin{cases} image[i+1, :]-image[i, :], &if\ 0<=i<h-1 \cr
+        0, &if\ i==h-1\end{cases}
+
+        dx[i] = \begin{cases} image[:, i+1]-image[:, i], &if\ 0<=i<w-1 \cr
+        0, &if\ i==w-1\end{cases}
+
+    Inputs:
+        - **images** (Tensor) - The input image data, with format 'NCHW'.
+
+    Outputs:
+        - **dy** (Tensor) - vertical image gradients, the same type and shape as input.
+        - **dx** (Tensor) - horizontal image gradients, the same type and shape as input.
+
+    Examples:
+        >>> net = nn.ImageGradients()
+        >>> image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
+        >>> net(image)
+        [[[[2,2]
+           [0,0]]]]
+        [[[[1,0]
+           [1,0]]]]
+    """
+    def __init__(self):
+        super(ImageGradients, self).__init__()
+
+    def construct(self, images):
+        batch_size, depth, height, width = P.Shape()(images)
+        dy = images[:, :, 1:, :] - images[:, :, :height - 1, :]
+        dy_last = P.Fill()(P.DType()(images), (batch_size, depth, 1, width), 0)
+        dy = P.Concat(2)((dy, dy_last))
+
+        dx = images[:, :, :, 1:] - images[:, :, :, :width - 1]
+        dx_last = P.Fill()(P.DType()(images), (batch_size, depth, height, 1), 0)
+        dx = P.Concat(3)((dx, dx_last))
+        return dy, dx

tests/st/ops/davinci/test_image_gradients.py

@@ -0,0 +1,62 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import numpy as np
+import mindspore.nn as nn
+import mindspore.context as context
+import mindspore.common.dtype as mstype
+from mindspore import Tensor
+from mindspore.common.api import ms_function
+
+context.set_context(device_target="Ascend")
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.image_gradients = nn.ImageGradients()
+
+    @ms_function
+    def construct(self, x):
+        return self.image_gradients(x)
+
+
+def test_image_gradients():
+    image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
+    expected_dy = np.array([[[[2,2],[0,0]]]]).astype(np.int32)
+    expected_dx = np.array([[[[1,0],[1,0]]]]).astype(np.int32)
+    net = Net()
+    dy, dx = net(image)
+    assert np.any(dx.asnumpy()-expected_dx) == False
+    assert np.any(dy.asnumpy()-expected_dy) == False
+
+
+def test_image_gradients_multi_channel_depth():
+    # 4 x 2 x 2 x 2
+    dtype = mstype.int32
+    image = Tensor(np.array([[[[1,2],[3,4]], [[5,6],[7,8]]],
+                             [[[3,5],[7,9]], [[11,13],[15,17]]],
+                             [[[5,10],[15,20]], [[25,30],[35,40]]],
+                             [[[10,20],[30,40]], [[50,60],[70,80]]]]), dtype=dtype)
+    expected_dy = Tensor(np.array([[[[2,2],[0,0]], [[2,2],[0,0]]],
+                                   [[[4,4],[0,0]], [[4,4],[0,0]]],
+                                   [[[10,10],[0,0]], [[10,10],[0,0]]],
+                                   [[[20,20],[0,0]], [[20,20],[0,0]]]]), dtype=dtype)
+    expected_dx = Tensor(np.array([[[[1,0],[1,0]], [[1,0],[1,0]]],
+                                   [[[2,0],[2,0]], [[2,0],[2,0]]],
+                                   [[[5,0],[5,0]], [[5,0],[5,0]]],
+                                   [[[10,0],[10,0]], [[10,0],[10,0]]]]), dtype=dtype)
+    net = Net()
+    dy, dx = net(image)
+
+    assert np.any(dx.asnumpy()-expected_dx.asnumpy()) == False
+    assert np.any(dy.asnumpy()-expected_dy.asnumpy()) == False

tests/ut/python/nn/test_image_gradients.py

@@ -0,0 +1,49 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+""" test loss """
+import numpy as np
+import mindspore.nn as nn
+import mindspore.context as context
+import mindspore.common.dtype as mstype
+from mindspore import Tensor
+from mindspore.common.api import _executor
+from mindspore.common.api import ms_function
+
+context.set_context(device_target="Ascend")
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.image_gradients = nn.ImageGradients()
+
+    @ms_function
+    def construct(self, x):
+        return self.image_gradients(x)
+
+def test_compile():
+    # input shape 1 x 1 x 2 x 2
+    image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
+    net = Net()
+    _executor.compile(net, image)
+
+
+def test_compile_multi_channel():
+    # input shape 4 x 2 x 2 x 2
+    dtype = mstype.int32
+    image = Tensor(np.array([[[[1,2],[3,4]], [[5,6],[7,8]]],
+                             [[[3,5],[7,9]], [[11,13],[15,17]]],
+                             [[[5,10],[15,20]], [[25,30],[35,40]]],
+                             [[[10,20],[30,40]], [[50,60],[70,80]]]]), dtype=dtype)
+    net = Net()
+    _executor.compile(net, image)