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124 lines
4.6 KiB
124 lines
4.6 KiB
# 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 numpy as np
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard
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import paddle
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import paddle.nn.functional as F
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import paddle.fluid as fluid
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def adaptive_start_index(index, input_size, output_size):
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return int(np.floor(index * input_size / output_size))
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def adaptive_end_index(index, input_size, output_size):
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return int(np.ceil((index + 1) * input_size / output_size))
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def avg_pool1D_forward_naive(x,
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ksize,
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strides,
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paddings,
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global_pool=0,
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ceil_mode=False,
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exclusive=False,
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adaptive=False,
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data_type=np.float64):
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N, C, L = x.shape
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if global_pool == 1:
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ksize = [L]
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if adaptive:
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L_out = ksize[0]
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else:
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L_out = (L - ksize[0] + 2 * paddings[0] + strides[0] - 1
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) // strides[0] + 1 if ceil_mode else (
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L - ksize[0] + 2 * paddings[0]) // strides[0] + 1
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out = np.zeros((N, C, L_out))
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for i in range(L_out):
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if adaptive:
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r_start = adaptive_start_index(i, L, ksize[0])
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r_end = adaptive_end_index(i, L, ksize[0])
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else:
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r_start = np.max((i * strides[0] - paddings[0], 0))
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r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L))
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x_masked = x[:, :, r_start:r_end]
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field_size = (r_end - r_start) \
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if (exclusive or adaptive) else (ksize[0])
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if data_type == np.int8 or data_type == np.uint8:
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out[:, :, i] = (np.rint(
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np.sum(x_masked, axis=(2, 3)) / field_size)).astype(data_type)
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else:
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out[:, :, i] = (np.sum(x_masked, axis=(2)) /
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field_size).astype(data_type)
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return out
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class TestPool1d_API(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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self.places.append(fluid.CUDAPlace(0))
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def check_adaptive_avg_dygraph_results(self, place):
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with fluid.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32]).astype("float32")
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input = fluid.dygraph.to_variable(input_np)
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result = F.adaptive_avg_pool1d(input, output_size=16)
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result_np = avg_pool1D_forward_naive(
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input_np, ksize=[16], strides=[0], paddings=[0], adaptive=True)
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self.assertTrue(np.allclose(result.numpy(), result_np))
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ada_max_pool1d_dg = paddle.nn.layer.AdaptiveAvgPool1d(
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output_size=16)
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result = ada_max_pool1d_dg(input)
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self.assertTrue(np.allclose(result.numpy(), result_np))
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result = paddle.nn.functional.common.interpolate(
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input, mode="area", size=16)
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self.assertTrue(np.allclose(result.numpy(), result_np))
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def check_adaptive_avg_static_results(self, place):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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input = fluid.data(name="input", shape=[2, 3, 32], dtype="float32")
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result = F.adaptive_avg_pool1d(input, output_size=16)
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input_np = np.random.random([2, 3, 32]).astype("float32")
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result_np = avg_pool1D_forward_naive(
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input_np, ksize=[16], strides=[2], paddings=[0], adaptive=True)
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exe = fluid.Executor(place)
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fetches = exe.run(fluid.default_main_program(),
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feed={"input": input_np},
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fetch_list=[result])
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self.assertTrue(np.allclose(fetches[0], result_np))
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def test_adaptive_avg_pool1d(self):
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for place in self.places:
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self.check_adaptive_avg_dygraph_results(place)
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self.check_adaptive_avg_static_results(place)
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if __name__ == '__main__':
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unittest.main()
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