chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2018 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 random
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import unittest
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import numpy as np
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from op_test import OpTest
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def seqconv(
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x,
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lod,
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filter,
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context_length,
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context_start,
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padding_trainable=False,
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padding_data=None,
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):
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[T, M] = x.shape
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col = np.zeros((T, context_length * M)).astype('float32')
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offset = [0]
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for seq_len in lod[0]:
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offset.append(offset[-1] + seq_len)
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begin_pad = np.max([0, -context_start])
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for i in range(len(offset) - 1):
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for j in range(context_length):
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in_begin = offset[i] + context_start + j
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in_end = offset[i + 1] + context_start + j
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out_begin = offset[i]
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out_end = offset[i + 1]
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if in_begin < offset[i]:
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pad_size = np.min(
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[offset[i] - in_begin, offset[i + 1] - offset[i]]
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)
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if padding_trainable:
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sub_w = padding_data[j : j + pad_size, :]
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col[
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offset[i] : offset[i] + pad_size, j * M : (j + 1) * M
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] = sub_w
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out_begin = offset[i] + pad_size
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in_begin = offset[i]
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if in_end > offset[i + 1]:
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pad_size = np.min(
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[in_end - offset[i + 1], offset[i + 1] - offset[i]]
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)
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if padding_trainable:
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sub_w = padding_data[
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begin_pad + context_start + j - pad_size : begin_pad
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+ context_start
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+ j,
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:,
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]
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col[
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offset[i + 1] - pad_size : offset[i + 1],
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j * M : (j + 1) * M,
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] = sub_w
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in_end = offset[i + 1]
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out_end = offset[i + 1] - pad_size
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if in_end <= in_begin:
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continue
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in_sub = x[in_begin:in_end, :]
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col[out_begin:out_end, j * M : (j + 1) * M] += in_sub
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return np.dot(col, filter)
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class TestSeqProject(OpTest):
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def setUp(self):
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self.init_test_case()
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self.op_type = 'sequence_conv'
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if (
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self.context_length == 1
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and self.context_start == 0
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and self.padding_trainable
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):
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print(
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"If context_start is 0 "
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"and context_length is 1,"
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" padding_trainable should be false."
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)
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return
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# one level, batch size
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x = np.random.uniform(
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0.1, 1, [self.input_size[0], self.input_size[1]]
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).astype('float32')
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w = np.random.uniform(
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0.1,
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1,
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[
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self.context_length * self.input_size[1],
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self.output_representation,
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],
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).astype('float32')
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begin_pad = np.max([0, -self.context_start])
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end_pad = np.max([0, self.context_start + self.context_length - 1])
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total_pad = begin_pad + end_pad
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padding_data = np.random.uniform(
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0.1, 1, [total_pad, self.input_size[1]]
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).astype('float32')
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self.pad_data = padding_data
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self.inputs = {
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'X': (x, self.lod),
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'Filter': w,
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}
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self.inputs_val = ['X', 'Filter']
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self.inputs_val_no_x = ['Filter']
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self.inputs_val_no_f = ['X']
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if total_pad != 0:
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self.inputs['PaddingData'] = padding_data
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self.inputs_val = ['X', 'PaddingData', 'Filter']
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self.inputs_val_no_x = ['PaddingData', 'Filter']
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self.inputs_val_no_f = ['PaddingData', 'X']
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self.attrs = {
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'contextStart': self.context_start,
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'contextLength': self.context_length,
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'paddingTrainable': self.padding_trainable,
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'contextStride': self.context_stride,
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}
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out = seqconv(
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x,
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self.lod,
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w,
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self.context_length,
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self.context_start,
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self.padding_trainable,
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self.pad_data,
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)
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self.outputs = {'Out': out}
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def test_check_output(self):
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# NODE(yjjiang11): This op will be deprecated.
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self.check_output(check_dygraph=False)
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def test_check_grad(self):
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if self.padding_trainable:
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self.check_grad(
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set(self.inputs_val),
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'Out',
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max_relative_error=0.05,
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check_dygraph=False,
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)
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def test_check_grad_input(self):
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self.check_grad(
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['X'],
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'Out',
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max_relative_error=0.05,
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no_grad_set=set(self.inputs_val_no_x),
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check_dygraph=False,
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)
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def test_check_grad_padding_data(self):
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if self.padding_trainable:
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self.check_grad(
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['PaddingData'],
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'Out',
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no_grad_set={'X', 'Filter'},
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check_dygraph=False,
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)
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def test_check_grad_Filter(self):
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self.check_grad(
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['Filter'],
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'Out',
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max_relative_error=0.05,
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no_grad_set=set(self.inputs_val_no_f),
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check_dygraph=False,
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)
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def test_check_grad_input_filter(self):
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if self.padding_trainable:
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self.check_grad(
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['X', 'Filter'],
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'Out',
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max_relative_error=0.05,
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no_grad_set={'PaddingData'},
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check_dygraph=False,
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)
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def test_check_grad_padding_input(self):
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if self.padding_trainable:
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self.check_grad(
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self.inputs_val_no_f,
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'Out',
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max_relative_error=0.05,
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no_grad_set={'Filter'},
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check_dygraph=False,
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)
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def test_check_grad_padding_filter(self):
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if self.padding_trainable:
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self.check_grad(
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self.inputs_val_no_x,
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'Out',
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max_relative_error=0.05,
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no_grad_set={'X'},
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check_dygraph=False,
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)
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def init_test_case(self):
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self.input_row = 11
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self.context_start = 0
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self.context_length = 1
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self.padding_trainable = False
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self.context_stride = 1
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self.input_size = [self.input_row, 23]
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offset_lod = [[0, 4, 5, 8, self.input_row]]
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self.lod = [[]]
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# convert from offset-based lod to length-based lod
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for i in range(len(offset_lod[0]) - 1):
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self.lod[0].append(offset_lod[0][i + 1] - offset_lod[0][i])
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self.output_representation = 8 # output feature size
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class TestSeqProjectCase1(TestSeqProject):
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def init_test_case(self):
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self.input_row = 11
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self.context_start = -1
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self.context_length = 3
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self.padding_trainable = True
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self.context_stride = 1
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self.input_size = [self.input_row, 50]
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offset_lod = [[0, 4, 5, 8, self.input_row]]
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self.lod = [[]]
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# convert from offset-based lod to length-based lod
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for i in range(len(offset_lod[0]) - 1):
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self.lod[0].append(offset_lod[0][i + 1] - offset_lod[0][i])
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self.output_representation = 8 # output feature size
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class TestSeqProjectCase2Len0(TestSeqProject):
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def init_test_case(self):
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self.input_row = 11
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self.context_start = -1
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self.context_length = 3
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self.padding_trainable = True
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self.context_stride = 1
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self.input_size = [self.input_row, 50]
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offset_lod = [[0, 0, 4, 5, 5, 8, self.input_row, self.input_row]]
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self.lod = [[]]
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# convert from offset-based lod to length-based lod
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for i in range(len(offset_lod[0]) - 1):
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self.lod[0].append(offset_lod[0][i + 1] - offset_lod[0][i])
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self.output_representation = 8 # output feature size
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class TestSeqProjectCase3(TestSeqProject):
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def init_test_case(self):
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self.input_row = 25
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self.context_start = 2
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self.context_length = 3
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self.padding_trainable = True
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self.context_stride = 1
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self.input_size = [self.input_row, 25]
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idx = list(range(self.input_size[0]))
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del idx[0]
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offset_lod = [
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[0, *np.sort(random.sample(idx, 8)).tolist(), self.input_size[0]]
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]
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self.lod = [[]]
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# convert from offset-based lod to length-based lod
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for i in range(len(offset_lod[0]) - 1):
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self.lod[0].append(offset_lod[0][i + 1] - offset_lod[0][i])
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self.output_representation = 8 # output feature size
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if __name__ == '__main__':
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unittest.main()
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