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- from __future__ import absolute_import
- import numpy as np
- from .Node import Op
- from ..gpu_links import conv2d_reduce_sum
-
-
- class Conv2d_ReduceSumOp(Op):
- def __init__(self, node_A, ctx=None):
- super().__init__(Conv2d_ReduceSumOp, [node_A], ctx)
-
- def compute(self, input_vals, output_val, stream_handle=None):
- if self.on_cpu:
- output_val[:] = np.sum(input_vals[0].asnumpy(), axis=(0, 2, 3))
- else:
- conv2d_reduce_sum(input_vals[0], output_val, stream_handle)
-
- def gradient(self, output_grad):
- from .Conv2dBroadcast import conv2d_broadcastto_op
- return [conv2d_broadcastto_op(output_grad, self.inputs[0], ctx=self.raw_ctx)]
-
- def infer_shape(self, input_shapes):
- """summation reduction axis = 0
- e.g. (3,4,5)->(4,5)
- for vector, simpler to do (3,)->(1,)
- """
- assert len(input_shapes) == 1
- channels = input_shapes[0][1]
- return (channels,)
-
-
- def conv2d_reducesum_op(node, ctx=None):
- """Creates a node that represents np.sum(node_A, axis=0).
- Only support common-case axis=0 reduction for simplicity of gradient.
-
- Parameters:
- ----
- node : Node
- The Node needed to be summed.
-
- Returns:
- ----
- A new Node instance created by Op.
-
- """
- return Conv2d_ReduceSumOp(node, ctx=ctx)
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