109 lines
3.3 KiB
Python
109 lines
3.3 KiB
Python
"""
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segment_sum
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top_k
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in_top_k
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logsumexp
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"""
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from keras_core import backend
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from keras_core.api_export import keras_core_export
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from keras_core.backend import KerasTensor
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from keras_core.backend import any_symbolic_tensors
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from keras_core.operations.operation import Operation
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from keras_core.operations.operation_utils import reduce_shape
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class SegmentSum(Operation):
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def __init__(self, num_segments=None, sorted=False):
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super().__init__()
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self.num_segments = num_segments
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self.sorted = sorted
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def compute_output_spec(self, data, segment_ids):
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num_segments = self.num_segments
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output_shape = (num_segments,) + tuple(data.shape[1:])
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return KerasTensor(shape=output_shape, dtype=data.dtype)
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def call(self, data, segment_ids):
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return backend.math.segment_sum(
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data,
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segment_ids,
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num_segments=self.num_segments,
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sorted=self.sorted,
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)
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@keras_core_export("keras_core.operations.segment_sum")
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def segment_sum(data, segment_ids, num_segments=None, sorted=False):
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if any_symbolic_tensors((data,)):
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return SegmentSum(num_segments, sorted).symbolic_call(data, segment_ids)
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return backend.math.segment_sum(
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data, segment_ids, num_segments=num_segments, sorted=sorted
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)
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class TopK(Operation):
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def __init__(self, k, sorted=False):
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super().__init__()
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self.k = k
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self.sorted = sorted
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def compute_output_spec(self, x):
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output_shape = list(x.shape)
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output_shape[-1] = self.k
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# Return a tuple (values, indices).
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return (
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KerasTensor(shape=output_shape, dtype=x.dtype),
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KerasTensor(shape=output_shape, dtype="int32"),
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)
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def call(self, x):
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return backend.math.top_k(x, self.k, self.sorted)
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@keras_core_export("keras_core.operations.top_k")
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def top_k(x, k, sorted=True):
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if any_symbolic_tensors((x,)):
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return TopK(k, sorted).symbolic_call(x)
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return backend.math.top_k(x, k, sorted)
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class InTopK(Operation):
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def __init__(self, k):
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super().__init__()
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self.k = k
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def compute_output_spec(self, targets, predictions):
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return KerasTensor(shape=targets.shape, dtype="bool")
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def call(self, targets, predictions):
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return backend.math.in_top_k(targets, predictions, self.k)
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@keras_core_export("keras_core.operations.in_top_k")
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def in_top_k(targets, predictions, k):
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if any_symbolic_tensors((targets, predictions)):
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return InTopK(k).symbolic_call(targets, predictions)
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return backend.math.in_top_k(targets, predictions, k)
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class Logsumexp(Operation):
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def __init__(self, axis=None, keepdims=False):
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super().__init__()
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self.axis = axis
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self.keepdims = keepdims
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def compute_output_spec(self, x):
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output_shape = reduce_shape(x.shape, self.axis, self.keepdims)
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return KerasTensor(shape=output_shape)
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def call(self, x):
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return backend.math.logsumexp(x, axis=self.axis, keepdims=self.keepdims)
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@keras_core_export("keras_core.operations.logsumexp")
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def logsumexp(x, axis=None, keepdims=False):
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if any_symbolic_tensors((x,)):
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return Logsumexp(axis, keepdims).symbolic_call(x)
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return backend.math.logsumexp(x, axis=axis, keepdims=keepdims)
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