112 lines
3.3 KiB
Python
112 lines
3.3 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import paddle
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from paddle.base.data_feeder import check_dtype, convert_dtype
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from paddle.base.framework import Variable
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def convert_out_size_to_list(out_size, op_type):
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"""
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Convert out_size(int, np.int32, np.int64, Variable) to list
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in imperative mode.
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"""
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if out_size is None:
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out_size = [0]
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elif isinstance(out_size, (int, np.int32, np.int64)):
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out_size = [out_size]
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elif isinstance(out_size, (Variable, paddle.pir.Value)):
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out_size.stop_gradient = True
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check_dtype(
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out_size.dtype,
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'out_size',
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['int32', 'int64'],
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'op_type',
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'(When type of out_size in' + op_type + ' is Variable.)',
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)
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if convert_dtype(out_size.dtype) == 'int64':
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out_size = paddle.cast(out_size, 'int32')
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else:
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out_size = [int(out_size)]
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return out_size
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def get_out_size_tensor_inputs(inputs, attrs, out_size, op_type):
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"""
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Convert out_size(int, np.int32, np.int64, Variable) to inputs
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and attrs in static graph mode.
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"""
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if out_size is None:
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attrs['out_size'] = [0]
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elif isinstance(out_size, (int, np.int32, np.int64)):
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attrs['out_size'] = [out_size]
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elif isinstance(out_size, Variable):
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out_size.stop_gradient = True
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check_dtype(
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out_size.dtype,
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'out_size',
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['int32', 'int64'],
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'op_type',
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'(When type of out_size in' + op_type + ' is Variable.)',
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)
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if convert_dtype(out_size.dtype) == 'int64':
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out_size = paddle.cast(out_size, 'int32')
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inputs["Out_size"] = out_size
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else:
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raise TypeError("Out_size only supports Variable or int.")
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def reshape_lhs_rhs(x, y):
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"""
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Expand dims to ensure there will be no broadcasting issues with different
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number of dimensions.
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"""
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if len(x.shape) == 1:
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x = paddle.reshape(x, [-1, 1])
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if len(y.shape) == 1:
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y = paddle.reshape(y, [-1, 1])
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x_shape = paddle.shape(x)
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y_shape = paddle.shape(y)
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if len(x.shape) != len(y.shape):
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max_ndims = max(len(x.shape), len(y.shape))
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x_pad_ndims = max_ndims - len(x.shape)
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y_pad_ndims = max_ndims - len(y.shape)
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new_x_shape = (
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[
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x_shape[0],
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]
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+ [
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1,
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]
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* x_pad_ndims
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+ list(x_shape[1:])
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)
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new_y_shape = (
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[
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y_shape[0],
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]
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+ [
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1,
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]
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* y_pad_ndims
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+ list(y_shape[1:])
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)
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x = paddle.reshape(x, new_x_shape)
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y = paddle.reshape(y, new_y_shape)
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return x, y
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