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- from __future__ import absolute_import
- from .Node import Op
- from .._base import DNNL_LIB
- from ..cpu_links import opposite as cpu_opposite
- from ..gpu_links import matrix_opposite
-
-
- class OppositeOp(Op):
- def __init__(self, node_A, ctx=None):
- super().__init__(OppositeOp, [node_A], ctx)
-
- def compute(self, input_vals, output_val, stream_handle=None):
- if self.on_cpu:
- if DNNL_LIB['DnnlOpposite']:
- cpu_opposite(input_vals[0], output_val)
- else:
- output_val[:] = -input_vals[0].asnumpy()
- else:
- matrix_opposite(input_vals[0], output_val, stream_handle)
-
- def gradient(self, output_grad):
- return [opposite_op(output_grad, ctx=self.raw_ctx)]
-
- def infer_shape(self, input_shapes):
- assert len(input_shapes) == 1
- return input_shapes[0]
-
-
- def opposite_op(node, ctx=None):
- """Calculate the opposite of a matrix elementwisely.
-
- Parameters:
- ----
- node : Node
- Input variable.
-
- Returns:
- ----
- A new Node instance created by Op.
-
- """
- return OppositeOp(node, ctx=ctx)
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