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- # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
-
- import unittest
-
- import numpy as np
-
- import paddle
- import paddle.nn.functional as F
- from paddle import base
- from paddle.base import core
- from paddle.pir_utils import test_with_pir_api
-
-
- def adaptive_start_index(index, input_size, output_size):
- return int(np.floor(index * input_size / output_size))
-
-
- def adaptive_end_index(index, input_size, output_size):
- return int(np.ceil((index + 1) * input_size / output_size))
-
-
- def max_pool1D_forward_naive(
- x,
- ksize,
- strides,
- paddings,
- global_pool=0,
- ceil_mode=False,
- exclusive=False,
- adaptive=False,
- data_type=np.float64,
- ):
- N, C, L = x.shape
- if global_pool == 1:
- ksize = [L]
- if adaptive:
- L_out = ksize[0]
- else:
- L_out = (
- (L - ksize[0] + 2 * paddings[0] + strides[0] - 1) // strides[0] + 1
- if ceil_mode
- else (L - ksize[0] + 2 * paddings[0]) // strides[0] + 1
- )
-
- out = np.zeros((N, C, L_out))
- for i in range(L_out):
- if adaptive:
- r_start = adaptive_start_index(i, L, ksize[0])
- r_end = adaptive_end_index(i, L, ksize[0])
- else:
- r_start = np.max((i * strides[0] - paddings[0], 0))
- r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L))
- x_masked = x[:, :, r_start:r_end]
-
- out[:, :, i] = np.max(x_masked, axis=(2))
- return out
-
-
- def avg_pool1D_forward_naive(
- x,
- ksize,
- strides,
- paddings,
- global_pool=0,
- ceil_mode=False,
- exclusive=False,
- adaptive=False,
- data_type=np.float64,
- ):
- N, C, L = x.shape
- if global_pool == 1:
- ksize = [L]
- if adaptive:
- L_out = ksize[0]
- else:
- L_out = (
- (L - ksize[0] + 2 * paddings[0] + strides[0] - 1) // strides[0] + 1
- if ceil_mode
- else (L - ksize[0] + 2 * paddings[0]) // strides[0] + 1
- )
-
- out = np.zeros((N, C, L_out))
- for i in range(L_out):
- if adaptive:
- r_start = adaptive_start_index(i, L, ksize[0])
- r_end = adaptive_end_index(i, L, ksize[0])
- else:
- r_start = np.max((i * strides[0] - paddings[0], 0))
- r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L))
- x_masked = x[:, :, r_start:r_end]
-
- field_size = (
- (r_end - r_start) if (exclusive or adaptive) else (ksize[0])
- )
- if data_type == np.int8 or data_type == np.uint8:
- out[:, :, i] = (
- np.rint(np.sum(x_masked, axis=(2, 3)) / field_size)
- ).astype(data_type)
- else:
- out[:, :, i] = (np.sum(x_masked, axis=(2)) / field_size).astype(
- data_type
- )
- return out
-
-
- class TestPool1D_API(unittest.TestCase):
- def setUp(self):
- np.random.seed(123)
- self.places = [base.CPUPlace()]
- if core.is_compiled_with_cuda():
- self.places.append(base.CUDAPlace(0))
-
- @test_with_pir_api
- def check_avg_static_results(self, place):
- with paddle.static.program_guard(paddle.static.Program()):
- input = paddle.static.data(
- name="input", shape=[2, 3, 32], dtype="float32"
- )
- result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=0)
-
- input_np = np.random.random([2, 3, 32]).astype("float32")
- result_np = avg_pool1D_forward_naive(
- input_np, ksize=[2], strides=[2], paddings=[0], ceil_mode=False
- )
-
- exe = paddle.static.Executor(place)
- fetches = exe.run(
- feed={"input": input_np},
- fetch_list=[result],
- )
- np.testing.assert_allclose(fetches[0], result_np, rtol=1e-05)
-
- @test_with_pir_api
- def check_avg_static_results_fp16(self, place):
- if core.is_compiled_with_cuda():
- with paddle.static.program_guard(paddle.static.Program()):
- input = paddle.static.data(
- name="input", shape=[2, 3, 32], dtype="float16"
- )
- result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=0)
-
- input_np = np.random.random([2, 3, 32]).astype("float16")
- result_np = avg_pool1D_forward_naive(
- input_np,
- ksize=[2],
- strides=[2],
- paddings=[0],
- ceil_mode=False,
- )
-
- place = paddle.CUDAPlace(0)
- exe = paddle.static.Executor(place)
- fetches = exe.run(
- feed={"input": input_np},
- fetch_list=[result],
- )
- np.testing.assert_allclose(fetches[0], result_np, rtol=1e-03)
-
- def check_avg_dygraph_results(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=[0])
-
- result_np = avg_pool1D_forward_naive(
- input_np, ksize=[2], strides=[2], paddings=[0]
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- avg_pool1d_dg = paddle.nn.layer.AvgPool1D(
- kernel_size=2, stride=None, padding=0
- )
- result = avg_pool1d_dg(input)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- def check_avg_dygraph_padding_results(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- result = F.avg_pool1d(
- input, kernel_size=2, stride=2, padding=[1], exclusive=True
- )
-
- result_np = avg_pool1D_forward_naive(
- input_np, ksize=[2], strides=[2], paddings=[1], exclusive=False
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- avg_pool1d_dg = paddle.nn.AvgPool1D(
- kernel_size=2, stride=None, padding=1, exclusive=True
- )
-
- result = avg_pool1d_dg(input)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- @test_with_pir_api
- def check_max_static_results(self, place):
- paddle.enable_static()
- with paddle.static.program_guard(
- paddle.static.Program(), paddle.static.Program()
- ):
- input = paddle.static.data(
- name="input", shape=[2, 3, 32], dtype="float32"
- )
- result = F.max_pool1d(input, kernel_size=2, stride=2, padding=[0])
-
- input_np = np.random.random([2, 3, 32]).astype("float32")
- result_np = max_pool1D_forward_naive(
- input_np, ksize=[2], strides=[2], paddings=[0]
- )
-
- exe = paddle.static.Executor(place)
- fetches = exe.run(
- feed={"input": input_np},
- fetch_list=[result],
- )
- np.testing.assert_allclose(fetches[0], result_np, rtol=1e-05)
-
- def check_max_dygraph_results(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- result = F.max_pool1d(input, kernel_size=2, stride=2, padding=0)
-
- result_np = max_pool1D_forward_naive(
- input_np, ksize=[2], strides=[2], paddings=[0]
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- max_pool1d_dg = paddle.nn.layer.MaxPool1D(
- kernel_size=2, stride=None, padding=0
- )
- result = max_pool1d_dg(input)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- def check_max_dygraph_return_index_results(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- result, index = F.max_pool1d(
- input, kernel_size=2, stride=2, padding=0, return_mask=True
- )
-
- result_np = max_pool1D_forward_naive(
- input_np, ksize=[2], strides=[2], paddings=[0]
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- max_pool1d_dg = paddle.nn.layer.MaxPool1D(
- kernel_size=2, stride=None, padding=0
- )
- result = max_pool1d_dg(input)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- def check_max_dygraph_padding_same(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- result = F.max_pool1d(
- input, kernel_size=2, stride=2, padding="SAME"
- )
-
- result_np = max_pool1D_forward_naive(
- input_np, ksize=[2], strides=[2], paddings=[0]
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- def check_avg_dygraph_padding_same(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- result = F.avg_pool1d(
- input, kernel_size=2, stride=2, padding="SAME"
- )
-
- result_np = avg_pool1D_forward_naive(
- input_np, ksize=[2], strides=[2], paddings=[0]
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- def test_pool1d(self):
- for place in self.places:
- self.check_max_dygraph_results(place)
- self.check_avg_dygraph_results(place)
- self.check_max_static_results(place)
- self.check_avg_static_results(place)
- self.check_max_dygraph_padding_same(place)
- self.check_avg_dygraph_padding_same(place)
- self.check_max_dygraph_return_index_results(place)
- self.check_avg_static_results_fp16(place)
-
-
- class TestPool1DError_API(unittest.TestCase):
- def test_error_api(self):
- def run1():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- padding = [[2]]
- res_pd = F.max_pool1d(
- input_pd, kernel_size=2, stride=2, padding=padding
- )
-
- self.assertRaises(ValueError, run1)
-
- def run2():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- padding = [[2]]
- res_pd = F.max_pool1d(
- input_pd, kernel_size=2, stride=2, padding=padding
- )
-
- self.assertRaises(ValueError, run2)
-
- def run3():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- padding = "padding"
- res_pd = F.max_pool1d(
- input_pd, kernel_size=2, stride=2, padding=padding
- )
-
- self.assertRaises(ValueError, run3)
-
- def run4():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- padding = "VALID"
- res_pd = F.max_pool1d(
- input_pd,
- kernel_size=2,
- stride=2,
- padding=padding,
- ceil_mode=True,
- )
-
- self.assertRaises(ValueError, run4)
-
- def run5():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- padding = "VALID"
- res_pd = F.max_pool1d(
- input_pd,
- kernel_size=2,
- stride=2,
- padding=padding,
- ceil_mode=True,
- )
-
- self.assertRaises(ValueError, run5)
-
- def run6():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- padding = "VALID"
- res_pd = F.avg_pool1d(
- input_pd,
- kernel_size=2,
- stride=2,
- padding=padding,
- ceil_mode=True,
- )
-
- self.assertRaises(ValueError, run6)
-
- def run7():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- padding = "paddle"
- res_pd = F.avg_pool1d(
- input_pd,
- kernel_size=2,
- stride=2,
- padding=padding,
- ceil_mode=True,
- )
-
- self.assertRaises(ValueError, run7)
-
- def run_kernel_out_of_range():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- padding = 0
- res_pd = F.avg_pool1d(
- input_pd,
- kernel_size=-1,
- stride=2,
- padding=padding,
- ceil_mode=True,
- )
-
- self.assertRaises(ValueError, run_kernel_out_of_range)
-
- def run_stride_out_of_range():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- padding = 0
- res_pd = F.avg_pool1d(
- input_pd,
- kernel_size=2,
- stride=0,
- padding=padding,
- ceil_mode=True,
- )
-
- self.assertRaises(ValueError, run_stride_out_of_range)
-
- def run_zero_stride():
- with base.dygraph.guard():
- array = np.array([1], dtype=np.float32)
- x = paddle.to_tensor(
- np.reshape(array, [1, 1, 1]), dtype='float32'
- )
- out = F.max_pool1d(
- x, 1, stride=0, padding=1, return_mask=True, ceil_mode=True
- )
-
- self.assertRaises(ValueError, run_zero_stride)
-
- def run_zero_tuple_stride():
- with base.dygraph.guard():
- array = np.array([1], dtype=np.float32)
- x = paddle.to_tensor(
- np.reshape(array, [1, 1, 1]), dtype='float32'
- )
- out = F.max_pool1d(x, 1, stride=(0))
-
- self.assertRaises(ValueError, run_zero_tuple_stride)
-
-
- if __name__ == '__main__':
- unittest.main()
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