wehub-resource-sync
960 lines
33 KiB
Python
960 lines
33 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 (
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get_device_place,
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get_places,
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is_custom_device,
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)
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import paddle
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import paddle.nn.functional as F
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from paddle import base
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from paddle.base import core
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def adaptive_start_index(index, input_size, output_size):
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return int(np.floor(index * input_size / output_size))
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def adaptive_end_index(index, input_size, output_size):
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return int(np.ceil((index + 1) * input_size / output_size))
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def max_pool1D_forward_naive(
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x,
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ksize,
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strides,
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paddings,
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global_pool=0,
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ceil_mode=False,
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exclusive=False,
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adaptive=False,
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data_type=np.float64,
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):
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N, C, L = x.shape
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if global_pool == 1:
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ksize = [L]
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if adaptive:
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L_out = ksize[0]
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else:
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L_out = (
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(L - ksize[0] + 2 * paddings[0] + strides[0] - 1) // strides[0] + 1
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if ceil_mode
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else (L - ksize[0] + 2 * paddings[0]) // strides[0] + 1
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)
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out = np.zeros((N, C, L_out))
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for i in range(L_out):
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if adaptive:
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r_start = adaptive_start_index(i, L, ksize[0])
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r_end = adaptive_end_index(i, L, ksize[0])
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else:
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r_start = np.max((i * strides[0] - paddings[0], 0))
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r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L))
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x_masked = x[:, :, r_start:r_end]
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out[:, :, i] = np.max(x_masked, axis=(2))
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return out
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def avg_pool1D_forward_naive(
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x,
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ksize,
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strides,
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paddings,
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global_pool=0,
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ceil_mode=False,
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exclusive=False,
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adaptive=False,
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data_type=np.float64,
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):
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N, C, L = x.shape
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if global_pool == 1:
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ksize = [L]
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if adaptive:
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L_out = ksize[0]
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else:
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L_out = (
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(L - ksize[0] + 2 * paddings[0] + strides[0] - 1) // strides[0] + 1
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if ceil_mode
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else (L - ksize[0] + 2 * paddings[0]) // strides[0] + 1
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)
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out = np.zeros((N, C, L_out))
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for i in range(L_out):
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if adaptive:
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r_start = adaptive_start_index(i, L, ksize[0])
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r_end = adaptive_end_index(i, L, ksize[0])
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else:
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r_start = np.max((i * strides[0] - paddings[0], 0))
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r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L))
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x_masked = x[:, :, r_start:r_end]
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field_size = (
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(r_end - r_start) if (exclusive or adaptive) else (ksize[0])
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)
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if data_type == np.int8 or data_type == np.uint8:
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out[:, :, i] = (
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np.rint(np.sum(x_masked, axis=(2, 3)) / field_size)
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).astype(data_type)
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else:
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out[:, :, i] = (np.sum(x_masked, axis=(2)) / field_size).astype(
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data_type
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)
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return out
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def lp_pool1D_forward_naive(
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x,
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ksize,
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strides,
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paddings,
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global_pool=0,
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ceil_mode=False,
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data_format='NCL',
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norm_type=None,
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):
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assert norm_type is not None
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if x.dtype == np.float16:
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x = x.astype(np.float32)
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if data_format == "NCL":
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N, C, L = x.shape
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else:
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N, L, C = x.shape
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if global_pool == 1:
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ksize = [L]
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L_out = (
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(L - ksize[0] + 2 * paddings[0] + strides[0] - 1) // strides[0] + 1
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if ceil_mode
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else (L - ksize[0] + 2 * paddings[0]) // strides[0] + 1
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)
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if data_format == "NCL":
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out = np.zeros((N, C, L_out))
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else:
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out = np.zeros((N, L_out, C))
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for i in range(L_out):
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r_start = np.max((i * strides[0] - paddings[0], 0))
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r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L))
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if data_format == "NCL":
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x_masked = x[:, :, r_start:r_end]
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else:
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x_masked = x[:, r_start:r_end, :]
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if data_format == "NCL":
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if norm_type == float('inf'):
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out[:, :, i] = np.max(x_masked, axis=(2))
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else:
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out[:, :, i] = np.power(
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np.sum(np.power(x_masked, norm_type), axis=(2)),
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1 / norm_type,
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)
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else:
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if norm_type == float('inf'):
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out[:, i, :] = np.max(x_masked, axis=(1))
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else:
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out[:, i, :] = np.power(
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np.sum(np.power(x_masked, norm_type), axis=(1)),
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1 / norm_type,
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)
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return out
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class TestPool1D_API(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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def check_avg_static_results(self, place):
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with paddle.static.program_guard(paddle.static.Program()):
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input = paddle.static.data(
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name="input", shape=[2, 3, 32], dtype="float32"
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)
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result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=0)
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input_np = np.random.random([2, 3, 32]).astype("float32")
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result_np = avg_pool1D_forward_naive(
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input_np, ksize=[2], strides=[2], paddings=[0], ceil_mode=False
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)
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exe = paddle.static.Executor(place)
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fetches = exe.run(
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feed={"input": input_np},
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fetch_list=[result],
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)
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np.testing.assert_allclose(fetches[0], result_np, rtol=1e-05)
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def check_avg_static_results_fp16(self, place):
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if core.is_compiled_with_cuda() or is_custom_device():
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with paddle.static.program_guard(paddle.static.Program()):
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input = paddle.static.data(
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name="input", shape=[2, 3, 32], dtype="float16"
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)
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result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=0)
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input_np = np.random.random([2, 3, 32]).astype("float16")
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result_np = avg_pool1D_forward_naive(
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input_np,
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ksize=[2],
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strides=[2],
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paddings=[0],
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ceil_mode=False,
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)
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place = get_device_place()
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exe = paddle.static.Executor(place)
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fetches = exe.run(
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feed={"input": input_np},
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fetch_list=[result],
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)
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np.testing.assert_allclose(fetches[0], result_np, rtol=1e-03)
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def check_avg_dygraph_results(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=[0])
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result_np = avg_pool1D_forward_naive(
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input_np, ksize=[2], strides=[2], paddings=[0]
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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avg_pool1d_dg = paddle.nn.layer.AvgPool1D(
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kernel_size=2, stride=None, padding=0
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)
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result = avg_pool1d_dg(input)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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def check_avg_dygraph_padding_results(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = F.avg_pool1d(
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input, kernel_size=2, stride=2, padding=[1], exclusive=True
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)
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result_np = avg_pool1D_forward_naive(
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input_np, ksize=[2], strides=[2], paddings=[1], exclusive=False
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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avg_pool1d_dg = paddle.nn.AvgPool1D(
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kernel_size=2, stride=None, padding=1, exclusive=True
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)
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result = avg_pool1d_dg(input)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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def check_max_static_results(self, place):
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paddle.enable_static()
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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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input = paddle.static.data(
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name="input", shape=[2, 3, 32], dtype="float32"
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)
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result = F.max_pool1d(input, kernel_size=2, stride=2, padding=[0])
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input_np = np.random.random([2, 3, 32]).astype("float32")
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result_np = max_pool1D_forward_naive(
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input_np, ksize=[2], strides=[2], paddings=[0]
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)
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exe = paddle.static.Executor(place)
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fetches = exe.run(
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feed={"input": input_np},
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fetch_list=[result],
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)
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np.testing.assert_allclose(fetches[0], result_np, rtol=1e-05)
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def check_max_dygraph_results(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = F.max_pool1d(input, kernel_size=2, stride=2, padding=0)
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result_np = max_pool1D_forward_naive(
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input_np, ksize=[2], strides=[2], paddings=[0]
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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# test param_two_alias(["x", "input"], ["return_mask", "return_indices"])
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result = F.max_pool1d(
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input=input,
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kernel_size=2,
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stride=2,
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padding=0,
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return_indices=False,
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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max_pool1d_dg = paddle.nn.layer.MaxPool1D(
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kernel_size=2, stride=None, padding=0
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)
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result = max_pool1d_dg(input)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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# test param_one_alias(["return_mask", "return_indices"])
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max_pool1d_dg = paddle.nn.layer.MaxPool1D(
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kernel_size=2, stride=None, padding=0, return_indices=False
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)
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result = max_pool1d_dg(input)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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def check_max_dygraph_return_index_results(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result, index = F.max_pool1d(
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input, kernel_size=2, stride=2, padding=0, return_mask=True
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)
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result_np = max_pool1D_forward_naive(
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input_np, ksize=[2], strides=[2], paddings=[0]
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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max_pool1d_dg = paddle.nn.layer.MaxPool1D(
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kernel_size=2, stride=None, padding=0
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)
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result = max_pool1d_dg(input)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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def check_max_dygraph_padding_same(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = F.max_pool1d(
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input, kernel_size=2, stride=2, padding="SAME"
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)
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result_np = max_pool1D_forward_naive(
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input_np, ksize=[2], strides=[2], paddings=[0]
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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def check_avg_dygraph_padding_same(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = F.avg_pool1d(
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input, kernel_size=2, stride=2, padding="SAME"
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)
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result_np = avg_pool1D_forward_naive(
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input_np, ksize=[2], strides=[2], paddings=[0]
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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def check_max_pool_return_mask_ceil(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([1, 3, 6]).astype("float32")
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input = paddle.to_tensor(input_np)
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result, _ = F.max_pool1d(
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input,
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kernel_size=5,
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stride=5,
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padding=0,
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ceil_mode=True,
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return_mask=True,
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)
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result_np = max_pool1D_forward_naive(
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input_np,
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ksize=[5],
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strides=[5],
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paddings=[0],
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ceil_mode=True,
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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self.assertEqual(result.shape, list(result_np.shape))
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def check_lp_static_results(self, place):
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with paddle.static.program_guard(paddle.static.Program()):
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input = paddle.static.data(
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name="input", shape=[2, 3, 32], dtype="float32"
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)
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result = F.lp_pool1d(
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input, norm_type=2, kernel_size=2, stride=2, padding=0
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)
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input_np = np.random.random([2, 3, 32]).astype("float32")
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result_np = lp_pool1D_forward_naive(
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input_np,
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ksize=[2],
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strides=[2],
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paddings=[0],
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ceil_mode=False,
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norm_type=2,
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)
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exe = paddle.static.Executor(place)
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fetches = exe.run(
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feed={"input": input_np},
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fetch_list=[result],
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)
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np.testing.assert_allclose(fetches[0], result_np, rtol=1e-05)
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def check_lp_static_results_fp16(self, place):
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if core.is_compiled_with_cuda() or is_custom_device():
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with paddle.static.program_guard(paddle.static.Program()):
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input = paddle.static.data(
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name="input", shape=[2, 3, 32], dtype="float16"
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)
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result = F.lp_pool1d(
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input, norm_type=3, kernel_size=2, stride=2, padding=0
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)
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input_np = np.random.random([2, 3, 32]).astype("float16")
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result_np = lp_pool1D_forward_naive(
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input_np,
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ksize=[2],
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strides=[2],
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paddings=[0],
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ceil_mode=False,
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norm_type=3,
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)
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place = get_device_place()
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exe = paddle.static.Executor(place)
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fetches = exe.run(
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feed={"input": input_np},
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fetch_list=[result],
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)
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np.testing.assert_allclose(
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fetches[0], result_np.astype(np.float16), rtol=1e-05
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)
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def check_lp_static_results_fp64(self, place):
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if core.is_compiled_with_cuda() or is_custom_device():
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with paddle.static.program_guard(paddle.static.Program()):
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input = paddle.static.data(
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name="input", shape=[2, 3, 32], dtype="float64"
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)
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result = F.lp_pool1d(
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input, norm_type=3, kernel_size=2, stride=2, padding=0
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)
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input_np = np.random.random([2, 3, 32]).astype("float64")
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result_np = lp_pool1D_forward_naive(
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input_np,
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ksize=[2],
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strides=[2],
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paddings=[0],
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ceil_mode=False,
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norm_type=3,
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)
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place = get_device_place()
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exe = paddle.static.Executor(place)
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fetches = exe.run(
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feed={"input": input_np},
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fetch_list=[result],
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)
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np.testing.assert_allclose(fetches[0], result_np, rtol=1e-05)
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def check_lp_dygraph_results(self, place):
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with base.dygraph.guard(place):
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|
input_np = np.random.random([2, 3, 32]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
result = F.lp_pool1d(
|
|
input, norm_type=4, kernel_size=3, stride=2, padding=[1]
|
|
)
|
|
|
|
result_np = lp_pool1D_forward_naive(
|
|
input_np,
|
|
ksize=[3],
|
|
strides=[2],
|
|
paddings=[1],
|
|
norm_type=4,
|
|
)
|
|
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
lp_pool1d_dg = paddle.nn.layer.LPPool1D(
|
|
norm_type=4, kernel_size=3, stride=2, padding=1
|
|
)
|
|
result = lp_pool1d_dg(input)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
def check_lp_dygraph_float16_results(self, place):
|
|
if isinstance(place, (base.CUDAPlace, base.CustomPlace)):
|
|
with base.dygraph.guard(place):
|
|
input_np = np.random.random([2, 3, 32]).astype("float16")
|
|
input = paddle.to_tensor(input_np)
|
|
result = F.lp_pool1d(
|
|
input, norm_type=5, kernel_size=5, stride=3, padding=[0]
|
|
)
|
|
|
|
result_np = lp_pool1D_forward_naive(
|
|
input_np, ksize=[5], strides=[3], paddings=[0], norm_type=5
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
result.numpy(), result_np.astype(np.float16), rtol=1e-05
|
|
)
|
|
|
|
lp_pool1d_dg = paddle.nn.layer.LPPool1D(
|
|
norm_type=5, kernel_size=5, stride=3, padding=0
|
|
)
|
|
result = lp_pool1d_dg(input)
|
|
np.testing.assert_allclose(
|
|
result.numpy(), result_np.astype(np.float16), rtol=1e-05
|
|
)
|
|
|
|
def check_lp_dygraph_float64_results(self, place):
|
|
if isinstance(place, (base.CUDAPlace, base.CustomPlace)):
|
|
with base.dygraph.guard(place):
|
|
input_np = np.random.random([2, 3, 32]).astype("float64")
|
|
input = paddle.to_tensor(input_np)
|
|
result = F.lp_pool1d(
|
|
input, norm_type=5, kernel_size=5, stride=3, padding=[0]
|
|
)
|
|
|
|
result_np = lp_pool1D_forward_naive(
|
|
input_np, ksize=[5], strides=[3], paddings=[0], norm_type=5
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
result.numpy(), result_np, rtol=1e-05
|
|
)
|
|
|
|
lp_pool1d_dg = paddle.nn.layer.LPPool1D(
|
|
norm_type=5, kernel_size=5, stride=3, padding=0
|
|
)
|
|
result = lp_pool1d_dg(input)
|
|
np.testing.assert_allclose(
|
|
result.numpy(), result_np, rtol=1e-05
|
|
)
|
|
|
|
def check_lp_dygraph_ceil_mode_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.lp_pool1d(
|
|
input,
|
|
norm_type=7,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=[1],
|
|
ceil_mode=True,
|
|
)
|
|
|
|
result_np = lp_pool1D_forward_naive(
|
|
input_np,
|
|
ksize=[2],
|
|
strides=[2],
|
|
paddings=[1],
|
|
ceil_mode=True,
|
|
norm_type=7,
|
|
)
|
|
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
lp_pool1d_dg = paddle.nn.LPPool1D(
|
|
norm_type=7,
|
|
kernel_size=2,
|
|
stride=None,
|
|
ceil_mode=True,
|
|
padding=1,
|
|
)
|
|
|
|
result = lp_pool1d_dg(input)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
def check_lp_dygraph_data_format_results(self, place):
|
|
with base.dygraph.guard(place):
|
|
input_np = np.random.random([2, 32, 3]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
result = F.lp_pool1d(
|
|
input,
|
|
norm_type=7,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=[1],
|
|
ceil_mode=True,
|
|
data_format="NLC",
|
|
)
|
|
|
|
result_np = lp_pool1D_forward_naive(
|
|
input_np,
|
|
ksize=[2],
|
|
strides=[2],
|
|
paddings=[1],
|
|
ceil_mode=True,
|
|
data_format="NLC",
|
|
norm_type=7,
|
|
)
|
|
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
lp_pool1d_dg = paddle.nn.LPPool1D(
|
|
norm_type=7,
|
|
kernel_size=2,
|
|
stride=None,
|
|
data_format="NLC",
|
|
padding=1,
|
|
)
|
|
|
|
result = lp_pool1d_dg(input)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
def check_lp_dygraph_inf_norm_type(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.lp_pool1d(
|
|
input,
|
|
norm_type=float('inf'),
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=[1],
|
|
ceil_mode=True,
|
|
)
|
|
|
|
result_np = lp_pool1D_forward_naive(
|
|
input_np,
|
|
ksize=[2],
|
|
strides=[2],
|
|
paddings=[1],
|
|
ceil_mode=True,
|
|
norm_type=float("inf"),
|
|
)
|
|
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
lp_pool1d_dg = paddle.nn.LPPool1D(
|
|
norm_type=float('inf'), kernel_size=2, stride=None, padding=1
|
|
)
|
|
|
|
result = lp_pool1d_dg(input)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
def check_lp_dygraph_compatibility(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.lp_pool1d(
|
|
input, norm_type=5, kernel_size=5, stride=3, padding=[0]
|
|
)
|
|
|
|
result_np = lp_pool1D_forward_naive(
|
|
input_np, ksize=[5], strides=[3], paddings=[0], norm_type=5
|
|
)
|
|
np.testing.assert_allclose(
|
|
result.numpy(), result_np.astype(np.float32), rtol=1e-05
|
|
)
|
|
# test input alias
|
|
result = F.lp_pool1d(
|
|
input=input, norm_type=5, kernel_size=5, stride=3, padding=[0]
|
|
)
|
|
np.testing.assert_allclose(
|
|
result.numpy(), result_np.astype(np.float32), rtol=1e-05
|
|
)
|
|
# test 5th positional argument with bool
|
|
result = F.lp_pool1d(input, 5, 5, 3, False)
|
|
np.testing.assert_allclose(
|
|
result.numpy(), result_np.astype(np.float32), rtol=1e-05
|
|
)
|
|
|
|
lp_pool1d_dg = paddle.nn.layer.LPPool1D(
|
|
norm_type=5, kernel_size=5, stride=3, padding=0
|
|
)
|
|
result = lp_pool1d_dg(input)
|
|
np.testing.assert_allclose(
|
|
result.numpy(), result_np.astype(np.float32), rtol=1e-05
|
|
)
|
|
|
|
lp_pool1d_dg = paddle.nn.LPPool1d(5, 5, 3, False)
|
|
result = lp_pool1d_dg(input=input)
|
|
np.testing.assert_allclose(
|
|
result.numpy(), result_np.astype(np.float32), 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)
|
|
self.check_max_pool_return_mask_ceil(place)
|
|
self.check_lp_static_results(place)
|
|
self.check_lp_dygraph_results(place)
|
|
self.check_lp_static_results_fp16(place)
|
|
self.check_lp_static_results_fp64(place)
|
|
self.check_lp_dygraph_inf_norm_type(place)
|
|
self.check_lp_dygraph_float16_results(place)
|
|
self.check_lp_dygraph_float64_results(place)
|
|
self.check_lp_dygraph_ceil_mode_results(place)
|
|
self.check_lp_dygraph_data_format_results(place)
|
|
self.check_lp_dygraph_compatibility(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)
|
|
|
|
|
|
class TestPool1D_API_ZeroSize(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(123)
|
|
self.places = get_places()
|
|
|
|
def check_avg_dygraph_results(self, place):
|
|
with base.dygraph.guard(place):
|
|
input_np = np.random.random([2, 0, 3]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
input.stop_gradient = False
|
|
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)
|
|
loss = paddle.sum(result)
|
|
loss.backward()
|
|
np.testing.assert_allclose(input.grad.shape, input.shape)
|
|
|
|
def check_max_dygraph_results(self, place):
|
|
with base.dygraph.guard(place):
|
|
# test1
|
|
input_np = np.random.random([2, 0, 3]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
input.stop_gradient = False
|
|
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)
|
|
loss = paddle.sum(result)
|
|
loss.backward()
|
|
np.testing.assert_allclose(input.grad.shape, input.shape)
|
|
# test2
|
|
input_np2 = np.random.random([2, 3, 0]).astype("float64")
|
|
input2 = paddle.to_tensor(input_np2)
|
|
input2.stop_gradient = False
|
|
result2 = F.max_pool1d(
|
|
input2, kernel_size=2, stride=1, padding=[1, 1]
|
|
)
|
|
# Torch result is 0.0
|
|
result_np2 = np.zeros([2, 3, 1], dtype=np.float64)
|
|
np.testing.assert_allclose(result2.numpy(), result_np2, rtol=1e-05)
|
|
loss2 = paddle.sum(result2)
|
|
loss2.backward()
|
|
np.testing.assert_allclose(input2.grad.shape, input2.shape)
|
|
|
|
def check_lp_dygraph_results(self, place):
|
|
with base.dygraph.guard(place):
|
|
input_np = np.random.random([2, 0, 3]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
input.stop_gradient = False
|
|
result = F.lp_pool1d(
|
|
input, norm_type=4, kernel_size=3, stride=2, padding=[1]
|
|
)
|
|
result_np = lp_pool1D_forward_naive(
|
|
input_np,
|
|
ksize=[3],
|
|
strides=[2],
|
|
paddings=[1],
|
|
norm_type=4,
|
|
)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
loss = paddle.sum(result)
|
|
loss.backward()
|
|
np.testing.assert_allclose(input.grad.shape, input.shape)
|
|
|
|
def test_pool1d(self):
|
|
for place in self.places:
|
|
self.check_max_dygraph_results(place)
|
|
self.check_avg_dygraph_results(place)
|
|
self.check_lp_dygraph_results(place)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|