Expose crop op into Python API. (#11546)

Author: qingqing01

python/paddle/fluid/layers/nn.py

@@ -93,6 +93,7 @@
  'mean_iou',
  'relu',
  'log',
+ 'crop',
 ]
 
 
@@ -5003,3 +5004,101 @@ def mean_iou(input, label, num_classes):
  },
  attrs={"num_classes": num_classes})
  return out_mean_iou, out_wrong, out_correct
+
+
+def crop(x, shape=None, offsets=None, name=None):
+ """
+ Crop input into output, as specified by offsets and shape.
+
+ .. code-block:: text
+
+ * Case 1:
+ Given
+ X = [[0, 1, 2, 0, 0]
+ [0, 3, 4, 0, 0]
+ [0, 0, 0, 0, 0]],
+ and
+ shape = [2, 2],
+ offsets = [0, 1],
+ output is:
+ Out = [[1, 2],
+ [3, 4]].
+ * Case 2:
+ Given
+ X = [[0, 1, 2, 5, 0]
+ [0, 3, 4, 6, 0]
+ [0, 0, 0, 0, 0]],
+ and shape is tensor
+ shape = [[0, 0, 0]
+ [0, 0, 0]]
+ and
+ offsets = [0, 1],
+
+ output is:
+ Out = [[1, 2, 5],
+ [3, 4, 6]].
+
+ Args:
+ x (Variable): The input tensor variable.
+ shape (Variable|list/tuple of integer): The output shape is specified
+ by `shape`, which can a Variable or a list/tupe of integer.
+ If a tensor Variable, it's rank must be the same as `x`. This way
+ is suitable for the case that the output shape may be changed each
+ iteration. If a list/tupe of integer, it's length must be the same
+ as the rank of `x`
+ offsets (Variable|list/tuple of integer|None): Specifies the copping
+ offsets at each dimension. It can be a Variable or or a list/tupe
+ of integer. If a tensor Variable, it's rank must be the same as `x`.
+ This way is suitable for the case that the offsets may be changed
+ each iteration. If a list/tupe of integer, it's length must be the
+ same as the rank of `x`. If None, the offsets are 0 at each
+ dimension.
+ name(str|None): A name for this layer(optional). If set None, the layer
+ will be named automatically.
+
+ Returns:
+ Variable: The cropped tensor variable.
+
+ Raises:
+ ValueError: If shape is not a list, tuple or Variable.
+
+ Examples:
+
+ .. code-block:: python
+
+ x = fluid.layers.data(name="x", shape=[3, 5], dtype="float32")
+ y = fluid.layers.data(name="y", shape=[2, 3], dtype="float32")
+ crop = fluid.layers.crop(x, shape=y)
+
+ # or
+ z = fluid.layers.data(name="z", shape=[3, 5], dtype="float32")
+ crop = fluid.layers.crop(z, shape=[2, 3])
+
+ """
+ helper = LayerHelper('crop', **locals())
+
+ if not (isinstance(shape, list) or isinstance(shape, tuple) or \
+ isinstance(shape, Variable)):
+ raise ValueError("The shape should be a list, tuple or Variable.")
+
+ if offsets is None:
+ offsets = [0] * len(x.shape)
+
+ out = helper.create_tmp_variable(x.dtype)
+ ipts = {'X': x}
+ attrs = {}
+ if isinstance(shape, Variable):
+ ipts['Y'] = shape
+ else:
+ attrs['shape'] = shape
+ if isinstance(offsets, Variable):
+ ipts['Offsets'] = offsets
+ else:
+ attrs['offsets'] = offsets
+
+ helper.append_op(
+ type='crop',
+ inputs=ipts,
+ outputs={'Out': out},
+ attrs=None if len(attrs) == 0 else attrs)
+ return out

python/paddle/fluid/tests/unittests/test_layers.py

@@ -401,6 +401,15 @@ def test_maxout(self):
  self.assertIsNotNone(output)
  print(str(program))
 
+ def test_maxout(self):
+ program = Program()
+ with program_guard(program):
+ x = layers.data(name='x', shape=[3, 5], dtype="float32")
+ y = layers.data(name='y', shape=[2, 3], dtype="float32")
+ output = layers.crop(x, shape=y)
+ self.assertIsNotNone(output)
+ print(str(program))
+
 
 if __name__ == '__main__':
  unittest.main()