wehub-resource-sync
217 lines
7.7 KiB
Python
217 lines
7.7 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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import paddle
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import paddle.nn.functional as F
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from paddle import base
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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(
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x,
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ksize,
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strides,
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paddings,
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global_pool=0,
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ceil_mode=False,
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exclusive=False,
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adaptive=False,
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data_type=np.float64,
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):
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N, C, L = x.shape
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if global_pool == 1:
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ksize = [L]
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if adaptive:
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L_out = ksize[0]
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else:
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L_out = (
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(L - ksize[0] + 2 * paddings[0] + strides[0] - 1) // strides[0] + 1
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if ceil_mode
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else (L - ksize[0] + 2 * paddings[0]) // strides[0] + 1
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)
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out = np.zeros((N, C, L_out))
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if x.size == 0:
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return out
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for i in range(L_out):
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if adaptive:
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r_start = adaptive_start_index(i, L, ksize[0])
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r_end = adaptive_end_index(i, L, ksize[0])
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else:
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r_start = np.max((i * strides[0] - paddings[0], 0))
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r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L))
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x_masked = x[:, :, r_start:r_end]
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field_size = (
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(r_end - r_start) if (exclusive or adaptive) else (ksize[0])
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)
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if data_type == np.int8 or data_type == np.uint8:
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out[:, :, i] = (
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np.rint(np.sum(x_masked, axis=(2, 3)) / field_size)
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).astype(data_type)
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else:
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out[:, :, i] = (np.sum(x_masked, axis=(2)) / field_size).astype(
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data_type
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)
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return out
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class TestPool1D_API(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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def check_adaptive_avg_dygraph_results(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([2, 3, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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result = F.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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)
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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 = F.adaptive_avg_pool1d(input=input, output_size=16)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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ada_max_pool1d_dg = paddle.nn.layer.AdaptiveAvgPool1D(
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output_size=16
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)
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result = ada_max_pool1d_dg(input)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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adaptive_avg_pool1d = paddle.nn.AdaptiveAvgPool1d(output_size=16)
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result = adaptive_avg_pool1d(input)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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result = paddle.nn.functional.common.interpolate(
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input, mode="area", size=16
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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_adaptive_avg_static_results(self, place):
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with base.program_guard(base.Program(), base.Program()):
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input = paddle.static.data(
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name="input", shape=[2, 3, 32], dtype="float32"
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)
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result1 = F.adaptive_avg_pool1d(input, output_size=16)
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# test param_one_alias(["x", "input"])
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result2 = F.adaptive_avg_pool1d(input=input, output_size=16)
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adaptive_avg_pool1d = paddle.nn.AdaptiveAvgPool1d(output_size=16)
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result3 = adaptive_avg_pool1d(input)
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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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)
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exe = base.Executor(place)
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fetches = exe.run(
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base.default_main_program(),
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feed={"input": input_np},
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fetch_list=[result1, result2, result3],
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)
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np.testing.assert_allclose(fetches[0], result_np, rtol=1e-05)
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np.testing.assert_allclose(fetches[1], result_np, rtol=1e-05)
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np.testing.assert_allclose(fetches[2], result_np, rtol=1e-05)
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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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class TestPool1D_API_ZeroSize(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_adaptive_avg_dygraph_results(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([0, 3, 32]).astype("float32")
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input = paddle.to_tensor(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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)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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ada_max_pool1d_dg = paddle.nn.layer.AdaptiveAvgPool1D(
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output_size=16
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)
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result = ada_max_pool1d_dg(input)
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np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
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result = paddle.nn.functional.common.interpolate(
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input, mode="area", size=16
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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_adaptive_avg_static_results(self, place):
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paddle.enable_static()
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with base.program_guard(base.Program(), base.Program()):
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input = paddle.static.data(
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name="input", shape=[0, 3, 32], dtype="float32"
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)
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result = F.adaptive_avg_pool1d(input, output_size=16)
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input_np = np.random.random([0, 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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)
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exe = base.Executor(place)
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fetches = exe.run(
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base.default_main_program(),
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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_grad(self, place):
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with base.dygraph.guard(place):
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input_np = np.random.random([0, 3, 32]).astype("float32")
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input = paddle.to_tensor(input_np)
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input.stop_gradient = False
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result = F.adaptive_avg_pool1d(input, output_size=16)
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loss = paddle.sum(result)
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loss.backward()
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np.testing.assert_allclose(input.grad.shape, input.shape)
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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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self.check_grad(place)
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if __name__ == '__main__':
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unittest.main()
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