wehub-resource-sync
435 lines
14 KiB
Python
435 lines
14 KiB
Python
# Copyright (c) 2020 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 unittest
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import numpy as np
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from op_test import OpTest, get_places
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import paddle
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from paddle import base
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from paddle.base import core
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def reference_unique_consecutive(
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X, return_inverse=False, return_counts=False, axis=None
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):
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"""
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Reference unique_consecutive implementation using python.
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Args:
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x(Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
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return_inverse(bool, optional): If True, also return the indices for where elements in
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the original input ended up in the returned unique consecutive tensor. Default is False.
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return_counts(bool, optional): If True, also return the counts for each unique consecutive element.
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"""
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X = list(X)
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is_empty = len(X) == 0
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counts_vec = [1] * len(X)
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i = 0
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counts = 1
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last = 0
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inverse_vec = [0] * len(X)
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if not is_empty:
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inverse_vec[last] = i
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cnt = 0
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while i < len(X) - 1:
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if X[i] == X[i + 1]:
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if return_counts:
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counts_vec[cnt] += 1
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del X[i]
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else:
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i += 1
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cnt += 1
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if return_inverse:
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last += 1
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inverse_vec[last] = i
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if return_counts:
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counts_vec = counts_vec[: len(X)]
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if return_inverse and return_counts:
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return X, np.array(inverse_vec), np.array(counts_vec)
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elif return_counts:
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return X, np.array(counts_vec)
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elif return_inverse:
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return X, np.array(inverse_vec)
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else:
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return X
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class TestUniqueConsecutiveOp(OpTest):
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"""case 1"""
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def config(self):
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self.x_size = 100
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self.x_range = 20
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self.return_inverse = False
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self.return_counts = False
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self.python_api = paddle.unique_consecutive
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def init_kernel_type(self):
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self.dtype = "float32" if core.is_compiled_with_rocm() else "float64"
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def setUp(self):
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self.init_kernel_type()
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self.config()
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self.op_type = "unique_consecutive"
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x = np.random.randint(self.x_range, size=self.x_size).astype(self.dtype)
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result = reference_unique_consecutive(
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x, self.return_inverse, self.return_counts
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)
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out = reference_unique_consecutive(x)
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out = np.array(out).astype(self.dtype)
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self.inputs = {
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'X': x,
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}
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self.python_out_sig = ["Out"]
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self.attrs = {'dtype': paddle.int32}
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self.outputs = {
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'Out': out,
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}
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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class TestUniqueConsecutiveOp2(TestUniqueConsecutiveOp):
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"""case 2"""
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def config(self):
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self.x_size = 100
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self.x_range = 20
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self.return_inverse = True
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self.return_counts = False
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self.python_api = paddle.unique_consecutive
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def setUp(self):
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self.init_kernel_type()
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self.config()
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self.op_type = "unique_consecutive"
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x = np.random.randint(self.x_range, size=self.x_size).astype(self.dtype)
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result, inverse = reference_unique_consecutive(
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x, self.return_inverse, self.return_counts
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)
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result = np.array(result).astype(self.dtype)
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inverse = inverse.astype(self.dtype)
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self.inputs = {
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'X': x,
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}
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self.attrs = {
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'return_inverse': self.return_inverse,
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'dtype': paddle.int32,
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}
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self.python_out_sig = ["Out", "Index"]
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self.outputs = {'Out': result, 'Index': inverse}
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class TestUniqueConsecutiveOp3(TestUniqueConsecutiveOp):
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"""case 3"""
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def config(self):
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self.x_size = 100
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self.x_range = 20
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self.return_inverse = False
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self.return_counts = True
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self.python_api = paddle.unique_consecutive
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def setUp(self):
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self.init_kernel_type()
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self.config()
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self.op_type = "unique_consecutive"
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x = np.random.randint(self.x_range, size=self.x_size).astype(self.dtype)
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result, counts = reference_unique_consecutive(
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x, self.return_inverse, self.return_counts
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)
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result = np.array(result).astype(self.dtype)
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counts = counts.astype(self.dtype)
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self.inputs = {
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'X': x,
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}
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self.attrs = {
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'return_counts': self.return_counts,
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'dtype': paddle.int32,
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}
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self.python_out_sig = ["Out", "Counts"]
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self.outputs = {'Out': result, 'Counts': counts}
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class TestUniqueConsecutiveOp4(TestUniqueConsecutiveOp):
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"""case 4"""
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def config(self):
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self.x_size = 100
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self.x_range = 20
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self.return_inverse = True
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self.return_counts = True
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self.python_api = paddle.unique_consecutive
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def setUp(self):
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self.init_kernel_type()
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self.config()
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self.op_type = "unique_consecutive"
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x = np.random.randint(self.x_range, size=self.x_size).astype(self.dtype)
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result, inverse, counts = reference_unique_consecutive(
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x, self.return_inverse, self.return_counts
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)
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result = np.array(result).astype(self.dtype)
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inverse = inverse.astype(self.dtype)
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counts = counts.astype(self.dtype)
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self.inputs = {
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'X': x,
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}
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self.attrs = {
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'return_inverse': self.return_inverse,
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'return_counts': self.return_counts,
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'dtype': paddle.int32,
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}
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self.python_out_sig = ["Out", "Index", "Counts"]
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self.outputs = {'Out': result, 'Index': inverse, 'Counts': counts}
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class TestUniqueConsecutiveAPI(unittest.TestCase):
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def setUp(self):
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self.places = get_places()
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def check_static_result(self, place):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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paddle.enable_static()
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input_x = paddle.static.data(
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name="input_x",
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shape=[
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100,
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],
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dtype="float32",
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)
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result = paddle.unique_consecutive(input_x)
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x_np = np.random.randint(20, size=100).astype("float32")
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exe = base.Executor(place)
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fetches = exe.run(
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feed={"input_x": x_np},
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fetch_list=[result],
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)
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def test_static(self):
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for place in self.places:
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self.check_static_result(place=place)
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
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input_x = np.random.randint(20, size=100).astype("float64")
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x = paddle.to_tensor(input_x)
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result = paddle.unique_consecutive(x)
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def test_dygraph_alias(self):
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for place in self.places:
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with base.dygraph.guard(place):
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input_x = np.random.randint(20, size=100).astype("float64")
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x = paddle.to_tensor(input_x)
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result = paddle.unique_consecutive(input=x)
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class TestUniqueConsecutiveCase2API(unittest.TestCase):
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def setUp(self):
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self.places = get_places()
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def check_static_result(self, place):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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paddle.enable_static()
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input_x = paddle.static.data(
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name="input_x",
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shape=[
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100,
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],
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dtype="float32",
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)
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result, inverse, counts = paddle.unique_consecutive(
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input_x, return_inverse=True, return_counts=True
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)
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x_np = np.random.randint(20, size=100).astype("float32")
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exe = base.Executor(place)
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fetches = exe.run(
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feed={"input_x": x_np},
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fetch_list=[result],
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)
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def test_static(self):
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for place in self.places:
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self.check_static_result(place=place)
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
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input_x = np.random.randint(20, size=100).astype("float64")
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x = paddle.to_tensor(input_x)
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result, inverse, counts = paddle.unique_consecutive(
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x, return_inverse=True, return_counts=True
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)
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class TestUniqueConsecutiveCase3API(unittest.TestCase):
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def setUp(self):
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self.places = get_places()
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def check_static_result(self, place):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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paddle.enable_static()
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input_x = paddle.static.data(
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name="input_x",
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shape=[
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100,
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],
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dtype="float32",
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)
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result, inverse, counts = paddle.unique_consecutive(
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input_x, return_inverse=True, return_counts=True, axis=-1
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)
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x_np = np.random.randint(20, size=100).astype("float32")
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exe = base.Executor(place)
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fetches = exe.run(
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feed={"input_x": x_np},
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fetch_list=[result],
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)
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def check_static_result_alias(self, place):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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paddle.enable_static()
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input_x = paddle.static.data(
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name="input_x",
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shape=[
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100,
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],
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dtype="float32",
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)
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result, inverse, counts = paddle.unique_consecutive(
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input=input_x, return_inverse=True, return_counts=True, axis=-1
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)
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x_np = np.random.randint(20, size=100).astype("float32")
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exe = base.Executor(place)
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fetches = exe.run(
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feed={"input_x": x_np},
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fetch_list=[result],
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)
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def test_static(self):
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for place in self.places:
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self.check_static_result(place=place)
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self.check_static_result_alias(place=place)
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
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input_x = np.random.randint(20, size=100).astype("float64")
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x = paddle.to_tensor(input_x)
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result, inverse, counts = paddle.unique_consecutive(
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x, return_inverse=True, return_counts=True, axis=-1
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)
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class TestUniqueConsecutiveEmptyInput(OpTest):
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"""empty input"""
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def config(self):
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self.return_inverse = True
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self.return_counts = True
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self.python_api = paddle.unique_consecutive
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def init_kernel_type(self):
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self.dtype = "float32" if core.is_compiled_with_rocm() else "float64"
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def setUp(self):
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self.init_kernel_type()
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self.config()
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self.op_type = "unique_consecutive"
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x = np.array([]).astype(self.dtype)
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result = reference_unique_consecutive(
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x, self.return_inverse, self.return_counts
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)
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out = reference_unique_consecutive(x)
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out = np.array(out).astype(self.dtype)
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self.inputs = {
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'X': x,
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}
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self.python_out_sig = ["Out"]
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self.attrs = {'dtype': paddle.int32}
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self.outputs = {
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'Out': out,
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}
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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class TestUniqueConsecutive_ZeroSize(OpTest):
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def config(self):
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self.python_api = paddle.unique_consecutive
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def init_kernel_type(self):
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self.dtype = "float32" if core.is_compiled_with_rocm() else "float64"
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def setUp(self):
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paddle.disable_static()
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self.init_kernel_type()
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self.config()
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self.op_type = "unique_consecutive"
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x = np.random.random([2, 0]).astype(self.dtype)
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out = np.array([]).astype(self.dtype)
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self.inputs = {
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'X': x,
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}
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self.python_out_sig = ["Out"]
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self.attrs = {'dtype': paddle.int32}
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self.outputs = {
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'Out': out,
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}
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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class TestFunctionalUniqueConsecutive(unittest.TestCase):
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def test_functional_unique_consecutive(self):
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with base.dygraph.guard():
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x_np = np.random.randint(20, size=[20]).astype("int32")
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x = paddle.tensor(x_np)
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out_expect = paddle.unique_consecutive(x)
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out_res = paddle.functional.unique_consecutive(x)
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np.testing.assert_equal(out_expect.numpy(), out_res.numpy())
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out_expect = paddle.unique_consecutive(
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x, return_inverse=True, return_counts=True
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)
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out_res = paddle.functional.unique_consecutive(
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x, return_inverse=True, return_counts=True
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)
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for expect, res in zip(out_expect, out_res):
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np.testing.assert_equal(expect.numpy(), res.numpy())
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x_np = np.random.randint(20, size=[20, 10]).astype("int32")
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x = paddle.tensor(x_np)
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out_expect = paddle.unique_consecutive(x, axis=1)
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out_res = paddle.functional.unique_consecutive(x, axis=1)
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np.testing.assert_equal(out_expect.numpy(), out_res.numpy())
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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