wehub-resource-sync
1191 lines
41 KiB
Python
1191 lines
41 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 get_places
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from test_pool2d_op import (
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avg_pool2D_forward_naive,
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max_pool2D_forward_naive,
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pool2D_forward_naive,
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)
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import paddle
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from paddle import base
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from paddle.nn.functional import avg_pool2d, lp_pool2d, max_pool2d
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class TestPool2D_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(
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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, 32], dtype="float32"
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)
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result = avg_pool2d(input, kernel_size=2, stride=2, padding=0)
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input_np = np.random.random([2, 3, 32, 32]).astype("float32")
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result_np = pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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strides=[2, 2],
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paddings=[0, 0],
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pool_type='avg',
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)
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exe = base.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_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, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = avg_pool2d(input, kernel_size=2, stride=2, padding=0)
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result_np = pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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strides=[2, 2],
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paddings=[0, 0],
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pool_type='avg',
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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avg_pool2d_dg = paddle.nn.layer.AvgPool2D(
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kernel_size=2, stride=2, padding=0
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)
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result = avg_pool2d_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, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = avg_pool2d(
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input, kernel_size=2, stride=2, padding=1, ceil_mode=False
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)
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result_np = avg_pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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strides=[2, 2],
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paddings=[1, 1],
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ceil_mode=False,
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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_pool2d_dg = paddle.nn.layer.AvgPool2D(
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kernel_size=2, stride=2, padding=1, ceil_mode=False
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)
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result = avg_pool2d_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_ceilmode_results(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = avg_pool2d(
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input, kernel_size=2, stride=2, padding=0, ceil_mode=True
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)
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result_np = avg_pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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strides=[2, 2],
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paddings=[0, 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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avg_pool2d_dg = paddle.nn.layer.AvgPool2D(
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kernel_size=2, stride=2, padding=0, ceil_mode=True
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)
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result = avg_pool2d_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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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, 32], dtype="float32"
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)
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result = max_pool2d(input, kernel_size=2, stride=2, padding=0)
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input_np = np.random.random([2, 3, 32, 32]).astype("float32")
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result_np = pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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strides=[2, 2],
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paddings=[0, 0],
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pool_type='max',
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)
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exe = base.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, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = max_pool2d(
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input, kernel_size=2, stride=2, padding=0, return_mask=False
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)
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result_np = pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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strides=[2, 2],
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paddings=[0, 0],
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pool_type='max',
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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 = max_pool2d(
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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_pool2d_dg = paddle.nn.layer.MaxPool2D(
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kernel_size=2, stride=2, padding=0
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)
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result = max_pool2d_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(["x", "input"])
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result = max_pool2d_dg(input=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_pool2d_dg = paddle.nn.layer.MaxPool2D(
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kernel_size=2, stride=2, padding=0, return_indices=False
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)
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result = max_pool2d_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_nhwc_results(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32, 32]).astype("float32")
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input = paddle.to_tensor(np.transpose(input_np, [0, 2, 3, 1]))
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result = max_pool2d(
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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_mask=False,
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data_format="NHWC",
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)
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result_np = pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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strides=[2, 2],
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paddings=[0, 0],
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pool_type='max',
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)
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np.testing.assert_allclose(
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np.transpose(result.numpy(), [0, 3, 1, 2]),
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result_np,
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rtol=1e-05,
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)
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def check_max_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, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = max_pool2d(
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input, kernel_size=2, stride=2, padding=1, ceil_mode=False
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)
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result_np = max_pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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strides=[2, 2],
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paddings=[1, 1],
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ceil_mode=False,
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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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max_pool2d_dg = paddle.nn.layer.MaxPool2D(
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kernel_size=2, stride=2, padding=1, ceil_mode=False
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)
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result = max_pool2d_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_ceilmode_results(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = max_pool2d(
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input, kernel_size=2, stride=2, padding=0, ceil_mode=True
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)
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result_np = max_pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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strides=[2, 2],
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paddings=[0, 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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max_pool2d_dg = paddle.nn.layer.MaxPool2D(
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kernel_size=2, stride=2, padding=0, ceil_mode=True
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)
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result = max_pool2d_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_stride_is_none(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result, indices = max_pool2d(
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input,
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kernel_size=2,
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stride=None,
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padding="SAME",
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return_mask=True,
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)
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result_np = pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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strides=[2, 2],
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paddings=[0, 0],
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pool_type='max',
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padding_algorithm="SAME",
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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max_pool2d_dg = paddle.nn.layer.MaxPool2D(
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kernel_size=2, stride=2, padding=0
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)
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result = max_pool2d_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_stride_is_none(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = avg_pool2d(
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input, kernel_size=2, stride=None, padding="SAME"
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)
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result_np = pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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strides=[2, 2],
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paddings=[0, 0],
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pool_type='avg',
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padding_algorithm="SAME",
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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avg_pool2d_dg = paddle.nn.layer.AvgPool2D(
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kernel_size=2, stride=2, padding=0
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)
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result = avg_pool2d_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(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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padding = [[0, 0], [0, 0], [0, 0], [0, 0]]
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result = max_pool2d(
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input,
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kernel_size=2,
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stride=2,
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padding=padding,
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return_mask=False,
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)
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result_np = pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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strides=[2, 2],
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paddings=[0, 0],
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pool_type='max',
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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max_pool2d_dg = paddle.nn.layer.MaxPool2D(
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kernel_size=2, stride=2, padding=0
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)
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result = max_pool2d_dg(input)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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def check_avg_divisor(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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padding = [[0, 0], [0, 0], [0, 0], [0, 0]]
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result = avg_pool2d(
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input,
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kernel_size=2,
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stride=2,
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padding=padding,
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divisor_override=4,
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)
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result_np = pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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strides=[2, 2],
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paddings=[0, 0],
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pool_type='avg',
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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avg_pool2d_dg = paddle.nn.layer.AvgPool2D(
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kernel_size=2, stride=2, padding=0
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)
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result = avg_pool2d_dg(input)
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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([2, 3, 33, 33]).astype("float32")
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input = paddle.to_tensor(input_np)
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result, _ = max_pool2d(
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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 = pool2D_forward_naive(
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input_np,
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ksize=[5, 5],
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strides=[5, 5],
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paddings=[0, 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(
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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, 128, 128], dtype="float32"
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)
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norm_type = 2
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result = lp_pool2d(
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input,
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norm_type,
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kernel_size=4,
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stride=4,
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ceil_mode=True,
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)
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input_np = np.random.random([2, 3, 128, 128]).astype("float32")
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result_np = pool2D_forward_naive(
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input_np,
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ksize=[4, 4],
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paddings=[0, 0],
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strides=[4, 4],
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ceil_mode=True,
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norm_type=norm_type,
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pool_type='lp',
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)
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exe = base.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, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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norm_type = 2
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result = lp_pool2d(
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input,
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norm_type,
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kernel_size=2,
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stride=1,
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ceil_mode=False,
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)
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result_np = pool2D_forward_naive(
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input_np,
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ksize=[2, 2],
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paddings=[0, 0],
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strides=[1, 1],
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ceil_mode=False,
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norm_type=norm_type,
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pool_type='lp',
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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# test input alias
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result = lp_pool2d(
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input=input,
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norm_type=norm_type,
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kernel_size=2,
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stride=1,
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ceil_mode=False,
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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# test 5th positional argument with bool
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result = lp_pool2d(
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input,
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norm_type,
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2,
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1,
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False,
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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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lp_pool2d_dg = paddle.nn.layer.LPPool2D(
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norm_type=norm_type,
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kernel_size=2,
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stride=1,
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ceil_mode=False,
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)
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result = lp_pool2d_dg(input)
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|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
lp_pool2d_dg = paddle.nn.LPPool2d(
|
|
norm_type,
|
|
2,
|
|
1,
|
|
False,
|
|
)
|
|
result = lp_pool2d_dg(input=input)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
def check_lp_dygraph_results_norm_type_is_inf(self, place):
|
|
with base.dygraph.guard(place):
|
|
input_np = np.random.random([2, 3, 32, 32]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
norm_type = np.inf
|
|
result = lp_pool2d(
|
|
input,
|
|
norm_type,
|
|
kernel_size=[2, 4],
|
|
stride=2,
|
|
ceil_mode=False,
|
|
)
|
|
|
|
result_np = pool2D_forward_naive(
|
|
input_np,
|
|
ksize=[2, 4],
|
|
paddings=[0, 0],
|
|
strides=[2, 2],
|
|
ceil_mode=False,
|
|
norm_type=norm_type,
|
|
pool_type='lp',
|
|
)
|
|
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
lp_pool2d_dg = paddle.nn.layer.LPPool2D(
|
|
norm_type=norm_type,
|
|
kernel_size=[2, 4],
|
|
stride=2,
|
|
ceil_mode=False,
|
|
)
|
|
result = lp_pool2d_dg(input)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
def check_lp_dygraph_results_norm_type_is_negative_inf(self, place):
|
|
with base.dygraph.guard(place):
|
|
input_np = np.random.random([2, 3, 32, 32]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
norm_type = -np.inf
|
|
result = lp_pool2d(
|
|
input,
|
|
norm_type,
|
|
kernel_size=2,
|
|
stride=2,
|
|
ceil_mode=False,
|
|
)
|
|
|
|
result_np = pool2D_forward_naive(
|
|
input_np,
|
|
ksize=[2, 2],
|
|
paddings=[0, 0],
|
|
strides=[2, 2],
|
|
ceil_mode=False,
|
|
norm_type=norm_type,
|
|
pool_type='lp',
|
|
)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
lp_pool2d_dg = paddle.nn.layer.LPPool2D(
|
|
norm_type=norm_type,
|
|
kernel_size=2,
|
|
stride=2,
|
|
ceil_mode=False,
|
|
)
|
|
result = lp_pool2d_dg(input)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
def check_lp_dygraph_ceilmode_results(self, place):
|
|
with base.dygraph.guard(place):
|
|
input_np = np.random.random([2, 3, 32, 32]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
norm_type = 2
|
|
result = lp_pool2d(
|
|
input,
|
|
norm_type,
|
|
kernel_size=5,
|
|
stride=3,
|
|
ceil_mode=True,
|
|
)
|
|
|
|
result_np = pool2D_forward_naive(
|
|
input_np,
|
|
ksize=[5, 5],
|
|
paddings=[0, 0],
|
|
strides=[3, 3],
|
|
ceil_mode=True,
|
|
norm_type=norm_type,
|
|
pool_type='lp',
|
|
)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
lp_pool2d_dg = paddle.nn.layer.LPPool2D(
|
|
norm_type=norm_type,
|
|
kernel_size=5,
|
|
stride=3,
|
|
ceil_mode=True,
|
|
)
|
|
result = lp_pool2d_dg(input)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
def check_lp_dygraph_nhwc_results(self, place):
|
|
with base.dygraph.guard(place):
|
|
input_np = np.random.random([2, 3, 32, 32]).astype("float32")
|
|
input = paddle.to_tensor(np.transpose(input_np, [0, 2, 3, 1]))
|
|
norm_type = 2
|
|
result = lp_pool2d(
|
|
input,
|
|
norm_type,
|
|
kernel_size=2,
|
|
stride=2,
|
|
ceil_mode=False,
|
|
data_format="NHWC",
|
|
)
|
|
result_np = pool2D_forward_naive(
|
|
input_np,
|
|
ksize=[2, 2],
|
|
paddings=[0, 0],
|
|
strides=[2, 2],
|
|
ceil_mode=False,
|
|
norm_type=norm_type,
|
|
pool_type='lp',
|
|
)
|
|
np.testing.assert_allclose(
|
|
np.transpose(result.numpy(), [0, 3, 1, 2]),
|
|
result_np,
|
|
rtol=1e-05,
|
|
)
|
|
lp_pool2d_dg = paddle.nn.layer.LPPool2D(
|
|
norm_type=norm_type,
|
|
kernel_size=2,
|
|
stride=[2, 2],
|
|
ceil_mode=False,
|
|
data_format="NHWC",
|
|
)
|
|
result = lp_pool2d_dg(input)
|
|
np.testing.assert_allclose(
|
|
np.transpose(result.numpy(), [0, 3, 1, 2]),
|
|
result_np,
|
|
rtol=1e-05,
|
|
)
|
|
|
|
def check_lp_dygraph_stride_is_none(self, place):
|
|
with base.dygraph.guard(place):
|
|
input_np = np.random.random([2, 3, 32, 32]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
norm_type = 2
|
|
result = lp_pool2d(
|
|
input,
|
|
norm_type,
|
|
kernel_size=2,
|
|
stride=None,
|
|
ceil_mode=False,
|
|
)
|
|
|
|
result_np = pool2D_forward_naive(
|
|
input_np,
|
|
paddings=[0, 0],
|
|
ksize=[2, 2],
|
|
strides=[2, 2],
|
|
ceil_mode=False,
|
|
norm_type=norm_type,
|
|
pool_type='lp',
|
|
)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
lp_pool2d_dg = paddle.nn.layer.LPPool2D(
|
|
norm_type=norm_type,
|
|
kernel_size=2,
|
|
stride=None,
|
|
ceil_mode=False,
|
|
)
|
|
result = lp_pool2d_dg(input)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
def check_lp_float16_static(self, place):
|
|
if isinstance(place, (base.CUDAPlace, base.CustomPlace)):
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
input = paddle.static.data(
|
|
name="input", shape=[2, 3, 64, 64], dtype="float16"
|
|
)
|
|
norm_type = 2
|
|
result = lp_pool2d(
|
|
input,
|
|
norm_type,
|
|
kernel_size=4,
|
|
stride=[2, 4],
|
|
ceil_mode=True,
|
|
)
|
|
|
|
input_np = np.random.random([2, 3, 64, 64]).astype("float16")
|
|
result_np = pool2D_forward_naive(
|
|
input_np,
|
|
ksize=[4, 4],
|
|
paddings=[0, 0],
|
|
strides=[2, 4],
|
|
ceil_mode=True,
|
|
norm_type=norm_type,
|
|
pool_type='lp',
|
|
)
|
|
|
|
exe = base.Executor(place)
|
|
fetches = exe.run(
|
|
feed={"input": input_np},
|
|
fetch_list=[result],
|
|
)
|
|
np.testing.assert_allclose(
|
|
fetches[0], result_np.astype(np.float16), rtol=1e-03
|
|
)
|
|
|
|
def check_lp_float64_static(self, place):
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
input = paddle.static.data(
|
|
name="input", shape=[2, 3, 64, 64], dtype="float64"
|
|
)
|
|
norm_type = 2
|
|
result = lp_pool2d(
|
|
input,
|
|
norm_type,
|
|
kernel_size=5,
|
|
stride=3,
|
|
ceil_mode=True,
|
|
)
|
|
|
|
input_np = np.random.random([2, 3, 64, 64]).astype("float64")
|
|
result_np = pool2D_forward_naive(
|
|
input_np,
|
|
ksize=[5, 5],
|
|
paddings=[0, 0],
|
|
strides=[3, 3],
|
|
ceil_mode=True,
|
|
norm_type=norm_type,
|
|
pool_type='lp',
|
|
)
|
|
|
|
exe = base.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_lp_dygraph_float16(self, place):
|
|
if isinstance(place, (base.CUDAPlace, base.CustomPlace)):
|
|
with base.dygraph.guard(place):
|
|
input_np = np.random.random([2, 3, 32, 32]).astype("float16")
|
|
input = paddle.to_tensor(input_np)
|
|
norm_type = 2
|
|
result = lp_pool2d(
|
|
input,
|
|
norm_type,
|
|
kernel_size=3,
|
|
stride=2,
|
|
ceil_mode=False,
|
|
)
|
|
|
|
result_np = pool2D_forward_naive(
|
|
input_np,
|
|
ksize=[3, 3],
|
|
paddings=[0, 0],
|
|
strides=[2, 2],
|
|
ceil_mode=False,
|
|
norm_type=norm_type,
|
|
pool_type='lp',
|
|
)
|
|
np.testing.assert_allclose(
|
|
result.numpy(), result_np, rtol=1e-03
|
|
)
|
|
|
|
lp_pool2d_dg = paddle.nn.layer.LPPool2D(
|
|
norm_type=norm_type,
|
|
kernel_size=3,
|
|
stride=2,
|
|
ceil_mode=False,
|
|
)
|
|
result = lp_pool2d_dg(input)
|
|
np.testing.assert_allclose(
|
|
result.numpy(), result_np.astype(np.float16), rtol=1e-03
|
|
)
|
|
|
|
def check_lp_dygraph_float64(self, place):
|
|
with base.dygraph.guard(place):
|
|
input_np = np.random.random([2, 3, 32, 32]).astype("float64")
|
|
input = paddle.to_tensor(input_np)
|
|
norm_type = 2
|
|
result = lp_pool2d(
|
|
input,
|
|
norm_type,
|
|
kernel_size=5,
|
|
stride=3,
|
|
ceil_mode=False,
|
|
)
|
|
|
|
result_np = pool2D_forward_naive(
|
|
input_np,
|
|
ksize=[5, 5],
|
|
paddings=[0, 0],
|
|
strides=[3, 3],
|
|
ceil_mode=False,
|
|
norm_type=norm_type,
|
|
pool_type='lp',
|
|
)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
lp_pool2d_dg = paddle.nn.layer.LPPool2D(
|
|
norm_type=norm_type,
|
|
kernel_size=5,
|
|
stride=3,
|
|
ceil_mode=False,
|
|
)
|
|
result = lp_pool2d_dg(input)
|
|
np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
|
|
|
|
def test_pool2d_static(self):
|
|
paddle.enable_static()
|
|
for place in self.places:
|
|
self.check_max_static_results(place)
|
|
self.check_avg_static_results(place)
|
|
self.check_lp_static_results(place)
|
|
self.check_lp_float64_static(place)
|
|
self.check_lp_float16_static(place)
|
|
paddle.disable_static()
|
|
|
|
def test_torch_compatible(self):
|
|
paddle.set_flags({'FLAGS_use_accuracy_compatible_kernel': 1})
|
|
paddle.enable_static()
|
|
for place in self.places:
|
|
self.check_max_static_results(place)
|
|
paddle.disable_static()
|
|
|
|
def test_pool2d(self):
|
|
for place in self.places:
|
|
self.check_max_dygraph_results(place)
|
|
self.check_avg_dygraph_results(place)
|
|
self.check_max_dygraph_stride_is_none(place)
|
|
self.check_avg_dygraph_stride_is_none(place)
|
|
self.check_max_dygraph_padding(place)
|
|
self.check_avg_divisor(place)
|
|
self.check_max_dygraph_padding_results(place)
|
|
self.check_max_dygraph_ceilmode_results(place)
|
|
self.check_max_dygraph_nhwc_results(place)
|
|
self.check_max_pool_return_mask_ceil(place)
|
|
self.check_lp_dygraph_results(place)
|
|
self.check_lp_dygraph_stride_is_none(place)
|
|
self.check_lp_dygraph_ceilmode_results(place)
|
|
self.check_lp_dygraph_nhwc_results(place)
|
|
self.check_lp_dygraph_results_norm_type_is_inf(place)
|
|
self.check_lp_dygraph_results_norm_type_is_negative_inf(place)
|
|
self.check_lp_dygraph_float64(place)
|
|
self.check_lp_dygraph_float16(place)
|
|
|
|
|
|
class TestPool2DError_API(unittest.TestCase):
|
|
def test_error_api(self):
|
|
def run1():
|
|
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 = [[0, 1], [0, 0], [0, 0], [0, 0]]
|
|
res_pd = max_pool2d(
|
|
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 = [[0, 1], [0, 0], [0, 0], [0, 0]]
|
|
res_pd = max_pool2d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=padding,
|
|
data_format='NHWC',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run2)
|
|
|
|
def run3():
|
|
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 = "padding"
|
|
res_pd = max_pool2d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=padding,
|
|
data_format='NHWC',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run3)
|
|
|
|
def run3_avg():
|
|
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 = "padding"
|
|
res_pd = avg_pool2d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=padding,
|
|
data_format='NHWC',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run3_avg)
|
|
|
|
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 = max_pool2d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=padding,
|
|
ceil_mode=True,
|
|
data_format='NHWC',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run4)
|
|
|
|
def run4_avg():
|
|
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 = avg_pool2d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=padding,
|
|
ceil_mode=True,
|
|
data_format='NHWC',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run4_avg)
|
|
|
|
def run5():
|
|
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 = "padding"
|
|
res_pd = avg_pool2d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=padding,
|
|
data_format='NHWC',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run5)
|
|
|
|
def run6():
|
|
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 = avg_pool2d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=padding,
|
|
ceil_mode=True,
|
|
data_format='NHWC',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run6)
|
|
|
|
def run7():
|
|
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 = avg_pool2d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=padding,
|
|
ceil_mode=False,
|
|
data_format='NNNN',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run7)
|
|
|
|
def run8():
|
|
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 = max_pool2d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=padding,
|
|
ceil_mode=False,
|
|
data_format='NNNN',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run8)
|
|
|
|
def run9():
|
|
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)
|
|
res_pd = max_pool2d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=0,
|
|
ceil_mode=False,
|
|
data_format='NHWC',
|
|
return_mask=True,
|
|
)
|
|
|
|
self.assertRaises(ValueError, run9)
|
|
|
|
def run_kernel_out_of_range():
|
|
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)
|
|
res_pd = avg_pool2d(
|
|
input_pd,
|
|
kernel_size=[-1, 2],
|
|
stride=2,
|
|
padding=0,
|
|
ceil_mode=False,
|
|
data_format='NHWC',
|
|
)
|
|
|
|
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, 32]).astype(
|
|
np.float32
|
|
)
|
|
input_pd = paddle.to_tensor(input_np)
|
|
res_pd = avg_pool2d(
|
|
input_pd,
|
|
kernel_size=3,
|
|
stride=[0, 2],
|
|
padding=0,
|
|
ceil_mode=False,
|
|
data_format='NHWC',
|
|
)
|
|
|
|
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, 1]), dtype='float32'
|
|
)
|
|
out = max_pool2d(
|
|
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, 1]), dtype='float32'
|
|
)
|
|
out = max_pool2d(
|
|
x, 1, stride=(0, 0), return_mask=False, data_format='NHWC'
|
|
)
|
|
|
|
self.assertRaises(ValueError, run_zero_tuple_stride)
|
|
|
|
def run_zero_norm_type():
|
|
with base.dygraph.guard():
|
|
array = np.array([1], dtype=np.float32)
|
|
x = paddle.to_tensor(
|
|
np.reshape(array, [1, 1, 1, 1]), dtype='float32'
|
|
)
|
|
out = lp_pool2d(x, 0, 2)
|
|
|
|
self.assertRaises(ValueError, run_zero_norm_type)
|
|
|
|
|
|
class TestPool2D_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, 3, 0, 0]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
input.stop_gradient = False
|
|
result = avg_pool2d(input, kernel_size=2, stride=2, padding=0)
|
|
|
|
result_np = pool2D_forward_naive(
|
|
input_np,
|
|
ksize=[2, 2],
|
|
strides=[2, 2],
|
|
paddings=[0, 0],
|
|
pool_type='avg',
|
|
)
|
|
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):
|
|
input_np = np.random.random([2, 3, 0, 0]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
input.stop_gradient = False
|
|
result = max_pool2d(
|
|
input, kernel_size=2, stride=2, padding=0, return_mask=False
|
|
)
|
|
|
|
result_np = pool2D_forward_naive(
|
|
input_np,
|
|
ksize=[2, 2],
|
|
strides=[2, 2],
|
|
paddings=[0, 0],
|
|
pool_type='max',
|
|
)
|
|
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_lp_dygraph_results(self, place):
|
|
with base.dygraph.guard(place):
|
|
input_np = np.random.random([2, 0, 32, 32]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
input.stop_gradient = False
|
|
norm_type = 2
|
|
result = lp_pool2d(
|
|
input,
|
|
norm_type,
|
|
kernel_size=2,
|
|
stride=1,
|
|
ceil_mode=False,
|
|
)
|
|
|
|
result_np = pool2D_forward_naive(
|
|
input_np,
|
|
ksize=[2, 2],
|
|
paddings=[0, 0],
|
|
strides=[1, 1],
|
|
ceil_mode=False,
|
|
norm_type=norm_type,
|
|
pool_type='lp',
|
|
)
|
|
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_pool2d(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()
|