wehub-resource-sync
740 lines
26 KiB
Python
740 lines
26 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_device_place, get_places, is_custom_device
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from test_pool3d_op import (
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avg_pool3D_forward_naive,
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max_pool3D_forward_naive,
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pool3D_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_pool3d, max_pool3d
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class TestPool3D_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, 32], dtype="float32"
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)
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result = avg_pool3d(input, kernel_size=2, stride=2, padding=0)
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input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
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result_np = pool3D_forward_naive(
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input_np,
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ksize=[2, 2, 2],
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strides=[2, 2, 2],
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paddings=[0, 0, 0],
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pool_type='avg',
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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_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, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = avg_pool3d(input, kernel_size=2, stride=2, padding="SAME")
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result_np = pool3D_forward_naive(
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input_np,
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ksize=[2, 2, 2],
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strides=[2, 2, 2],
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paddings=[0, 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_pool3d_dg = paddle.nn.layer.AvgPool3D(
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kernel_size=2, stride=None, padding="SAME"
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)
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result = avg_pool3d_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, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = avg_pool3d(
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input,
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kernel_size=2,
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stride=2,
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padding=1,
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ceil_mode=False,
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exclusive=True,
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)
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result_np = avg_pool3D_forward_naive(
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input_np,
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ksize=[2, 2, 2],
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strides=[2, 2, 2],
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paddings=[1, 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_pool3d_dg = paddle.nn.layer.AvgPool3D(
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kernel_size=2,
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stride=None,
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padding=1,
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ceil_mode=False,
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exclusive=True,
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)
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result = avg_pool3d_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, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = avg_pool3d(
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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_pool3D_forward_naive(
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input_np,
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ksize=[2, 2, 2],
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strides=[2, 2, 2],
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paddings=[0, 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_pool3d_dg = paddle.nn.layer.AvgPool3D(
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kernel_size=2, stride=None, padding=0, ceil_mode=True
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)
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result = avg_pool3d_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, 32], dtype="float32"
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)
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result = max_pool3d(input, kernel_size=2, stride=2, padding=0)
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input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
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result_np = pool3D_forward_naive(
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input_np,
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ksize=[2, 2, 2],
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strides=[2, 2, 2],
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paddings=[0, 0, 0],
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pool_type='max',
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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, 32, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = max_pool3d(input, kernel_size=2, stride=2, padding=0)
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result_np = pool3D_forward_naive(
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input_np,
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ksize=[2, 2, 2],
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strides=[2, 2, 2],
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paddings=[0, 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_pool3d(
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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_pool3d_dg = paddle.nn.layer.MaxPool3D(
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kernel_size=2, stride=None, padding=0
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)
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result = max_pool3d_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_pool3d_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_pool3d_dg = paddle.nn.layer.MaxPool3D(
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kernel_size=2, stride=None, padding=0, return_indices=False
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)
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result = max_pool3d_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_ndhwc_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, 32]).astype("float32")
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input = paddle.to_tensor(np.transpose(input_np, [0, 2, 3, 4, 1]))
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result = max_pool3d(
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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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data_format="NDHWC",
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return_mask=False,
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)
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result_np = pool3D_forward_naive(
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input_np,
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ksize=[2, 2, 2],
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strides=[2, 2, 2],
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paddings=[0, 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, 4, 1, 2, 3]),
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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_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, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = max_pool3d(
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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_pool3D_forward_naive(
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input_np,
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ksize=[2, 2, 2],
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strides=[2, 2, 2],
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paddings=[0, 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_pool3d_dg = paddle.nn.layer.MaxPool3D(
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kernel_size=2, stride=None, padding=0, ceil_mode=True
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)
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result = max_pool3d_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_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, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = max_pool3d(
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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_pool3D_forward_naive(
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input_np,
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ksize=[2, 2, 2],
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strides=[2, 2, 2],
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paddings=[1, 1, 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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max_pool3d_dg = paddle.nn.layer.MaxPool3D(
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kernel_size=2, stride=None, padding=1, ceil_mode=False
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)
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result = max_pool3d_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, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result, indices = max_pool3d(
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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 = pool3D_forward_naive(
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input_np,
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ksize=[2, 2, 2],
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strides=[2, 2, 2],
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paddings=[0, 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_pool3d_dg = paddle.nn.layer.MaxPool3D(
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kernel_size=2, stride=2, padding=0
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)
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result = max_pool3d_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, 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], [0, 0]]
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result = max_pool3d(input, kernel_size=2, stride=2, padding=padding)
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result_np = pool3D_forward_naive(
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input_np,
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ksize=[2, 2, 2],
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strides=[2, 2, 2],
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paddings=[0, 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_pool3d_dg = paddle.nn.layer.MaxPool3D(
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kernel_size=2, stride=2, padding=0
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)
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result = max_pool3d_dg(input)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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padding = [0, 0, 0, 0, 0, 0]
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result = max_pool3d(input, kernel_size=2, stride=2, padding=padding)
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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, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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padding = 0
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result = avg_pool3d(
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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=8,
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)
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result_np = pool3D_forward_naive(
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input_np,
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ksize=[2, 2, 2],
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strides=[2, 2, 2],
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paddings=[0, 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_pool3d_dg = paddle.nn.layer.AvgPool3D(
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kernel_size=2, stride=2, padding=0
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)
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result = avg_pool3d_dg(input)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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padding = [0, 0, 0, 0, 0, 0]
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result = avg_pool3d(
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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=8,
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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, 2, 6, 33, 33]).astype("float32")
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input = paddle.to_tensor(input_np)
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result, _ = max_pool3d(
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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 = pool3D_forward_naive(
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input_np,
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ksize=[5, 5, 5],
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strides=[5, 5, 5],
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paddings=[0, 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 test_pool3d(self):
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paddle.enable_static()
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for place in self.places:
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self.check_max_dygraph_results(place)
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self.check_avg_dygraph_results(place)
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self.check_max_static_results(place)
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self.check_avg_static_results(place)
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self.check_max_dygraph_stride_is_none(place)
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self.check_max_dygraph_padding(place)
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self.check_avg_divisor(place)
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self.check_max_dygraph_ndhwc_results(place)
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self.check_max_dygraph_ceilmode_results(place)
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self.check_max_pool_return_mask_ceil(place)
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def test_static_fp16_gpu(self):
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paddle.enable_static()
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_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 = np.random.random([1, 2, 3, 32, 32]).astype("float16")
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x = paddle.static.data(
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name="x", shape=[1, 2, 3, 32, 32], dtype="float16"
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)
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m = paddle.nn.AvgPool3D(kernel_size=2, stride=2, padding=0)
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y = m(x)
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exe = paddle.static.Executor(place)
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res = exe.run(
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feed={
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"x": input,
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},
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fetch_list=[y],
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)
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np.testing.assert_array_equal(res[0].shape, [1, 2, 1, 16, 16])
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def test_static_bf16_gpu(self):
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paddle.enable_static()
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if (
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paddle.base.core.is_compiled_with_cuda() or is_custom_device()
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) and paddle.base.core.is_bfloat16_supported(get_device_place()):
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place = get_device_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 = np.random.random([1, 2, 3, 32, 32]).astype(np.uint16)
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x = paddle.static.data(
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name="x", shape=[1, 2, 3, 32, 32], dtype="bfloat16"
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)
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m = paddle.nn.AvgPool3D(kernel_size=2, stride=2, padding=0)
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y = m(x)
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exe = paddle.static.Executor(place)
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res = exe.run(
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feed={
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"x": input,
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},
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fetch_list=[y],
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)
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np.testing.assert_array_equal(res[0].shape, [1, 2, 1, 16, 16])
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class TestPool3DError_API(unittest.TestCase):
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def test_error_api(self):
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def run1():
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with base.dygraph.guard():
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input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
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np.float32
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)
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input_pd = paddle.to_tensor(input_np)
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padding = [[0, 1], [0, 0], [0, 0], [0, 0], [0, 0]]
|
|
res_pd = avg_pool3d(
|
|
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, 32]).astype(
|
|
np.float32
|
|
)
|
|
input_pd = paddle.to_tensor(input_np)
|
|
padding = [[0, 1], [0, 0], [0, 0], [0, 0], [0, 0]]
|
|
res_pd = avg_pool3d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=padding,
|
|
data_format='NCDHW',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run2)
|
|
|
|
def run3():
|
|
with base.dygraph.guard():
|
|
input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
|
|
np.float32
|
|
)
|
|
input_pd = paddle.to_tensor(input_np)
|
|
padding = [[0, 1], [0, 0], [0, 0], [0, 0], [0, 0]]
|
|
res_pd = avg_pool3d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=padding,
|
|
data_format='NDHWC',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run3)
|
|
|
|
def run4():
|
|
with base.dygraph.guard():
|
|
input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
|
|
np.float32
|
|
)
|
|
input_pd = paddle.to_tensor(input_np)
|
|
res_pd = avg_pool3d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=0,
|
|
data_format='NNNN',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run4)
|
|
|
|
def run5():
|
|
with base.dygraph.guard():
|
|
input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
|
|
np.float32
|
|
)
|
|
input_pd = paddle.to_tensor(input_np)
|
|
res_pd = max_pool3d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=0,
|
|
data_format='NNNN',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run5)
|
|
|
|
def run6():
|
|
with base.dygraph.guard():
|
|
input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
|
|
np.float32
|
|
)
|
|
input_pd = paddle.to_tensor(input_np)
|
|
res_pd = avg_pool3d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding="padding",
|
|
data_format='NNNN',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run6)
|
|
|
|
def run7():
|
|
with base.dygraph.guard():
|
|
input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
|
|
np.float32
|
|
)
|
|
input_pd = paddle.to_tensor(input_np)
|
|
res_pd = max_pool3d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding="padding",
|
|
data_format='NNNN',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run7)
|
|
|
|
def run8():
|
|
with base.dygraph.guard():
|
|
input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
|
|
np.float32
|
|
)
|
|
input_pd = paddle.to_tensor(input_np)
|
|
res_pd = avg_pool3d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding="VALID",
|
|
ceil_mode=True,
|
|
data_format='NNNN',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run8)
|
|
|
|
def run9():
|
|
with base.dygraph.guard():
|
|
input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
|
|
np.float32
|
|
)
|
|
input_pd = paddle.to_tensor(input_np)
|
|
res_pd = max_pool3d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding="VALID",
|
|
ceil_mode=True,
|
|
data_format='NNNN',
|
|
)
|
|
|
|
self.assertRaises(ValueError, run9)
|
|
|
|
def run10():
|
|
with base.dygraph.guard():
|
|
input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
|
|
np.float32
|
|
)
|
|
input_pd = paddle.to_tensor(input_np)
|
|
res_pd = max_pool3d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=0,
|
|
data_format='NDHWC',
|
|
return_mask=True,
|
|
)
|
|
|
|
self.assertRaises(ValueError, run10)
|
|
|
|
def run_kernel_out_of_range():
|
|
with base.dygraph.guard():
|
|
input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
|
|
np.float32
|
|
)
|
|
input_pd = paddle.to_tensor(input_np)
|
|
res_pd = avg_pool3d(
|
|
input_pd,
|
|
kernel_size=-1,
|
|
stride=2,
|
|
padding="VALID",
|
|
ceil_mode=True,
|
|
)
|
|
|
|
self.assertRaises(ValueError, run_kernel_out_of_range)
|
|
|
|
def run_size_out_of_range():
|
|
with base.dygraph.guard():
|
|
input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
|
|
np.float32
|
|
)
|
|
input_pd = paddle.to_tensor(input_np)
|
|
res_pd = avg_pool3d(
|
|
input_pd,
|
|
kernel_size=2,
|
|
stride=0,
|
|
padding="VALID",
|
|
ceil_mode=True,
|
|
)
|
|
|
|
self.assertRaises(ValueError, run_size_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, 1]), dtype='float32'
|
|
)
|
|
out = max_pool3d(
|
|
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, 1]), dtype='float32'
|
|
)
|
|
out = max_pool3d(x, 1, stride=(0, 0, 0), ceil_mode=False)
|
|
|
|
self.assertRaises(ValueError, run_zero_tuple_stride)
|
|
|
|
|
|
class TestPool3D_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, 32, 32]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
input.stop_gradient = False
|
|
result = avg_pool3d(input, kernel_size=2, stride=2, padding="SAME")
|
|
|
|
result_np = pool3D_forward_naive(
|
|
input_np,
|
|
ksize=[2, 2, 2],
|
|
strides=[2, 2, 2],
|
|
paddings=[0, 0, 0],
|
|
pool_type='avg',
|
|
padding_algorithm="SAME",
|
|
)
|
|
|
|
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, 32, 32]).astype("float32")
|
|
input = paddle.to_tensor(input_np)
|
|
input.stop_gradient = False
|
|
result = max_pool3d(input, kernel_size=2, stride=2, padding=0)
|
|
|
|
result_np = pool3D_forward_naive(
|
|
input_np,
|
|
ksize=[2, 2, 2],
|
|
strides=[2, 2, 2],
|
|
paddings=[0, 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 test_pool3d(self):
|
|
paddle.enable_static()
|
|
for place in self.places:
|
|
self.check_max_dygraph_results(place)
|
|
self.check_avg_dygraph_results(place)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|