wehub-resource-sync
608 lines
21 KiB
Python
608 lines
21 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 os
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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, is_custom_device
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from test_attribute_var import UnittestBase
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import paddle
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from paddle.base import core
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from paddle.framework import in_pir_mode
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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 adaptive_pool2d_forward(
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x, output_size, data_format='NCHW', pool_type="avg"
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):
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N = x.shape[0]
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C, H, W = (
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[x.shape[1], x.shape[2], x.shape[3]]
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if data_format == 'NCHW'
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else [x.shape[3], x.shape[1], x.shape[2]]
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)
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if isinstance(output_size, int) or output_size is None:
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H_out = output_size
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W_out = output_size
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output_size = [H_out, W_out]
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else:
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H_out, W_out = output_size
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if output_size[0] is None:
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output_size[0] = H
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H_out = H
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if output_size[1] is None:
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output_size[1] = W
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W_out = W
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out = (
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np.zeros((N, C, H_out, W_out))
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if data_format == 'NCHW'
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else np.zeros((N, H_out, W_out, C))
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)
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if x.size == 0:
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return out
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for i in range(H_out):
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in_h_start = adaptive_start_index(i, H, output_size[0])
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in_h_end = adaptive_end_index(i, H, output_size[0])
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for j in range(W_out):
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in_w_start = adaptive_start_index(j, W, output_size[1])
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in_w_end = adaptive_end_index(j, W, output_size[1])
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if data_format == 'NCHW':
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x_masked = x[:, :, in_h_start:in_h_end, in_w_start:in_w_end]
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if pool_type == 'avg':
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field_size = (in_h_end - in_h_start) * (
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in_w_end - in_w_start
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)
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out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size
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elif pool_type == 'max':
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out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
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elif data_format == 'NHWC':
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x_masked = x[:, in_h_start:in_h_end, in_w_start:in_w_end, :]
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if pool_type == 'avg':
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field_size = (in_h_end - in_h_start) * (
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in_w_end - in_w_start
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)
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out[:, i, j, :] = np.sum(x_masked, axis=(1, 2)) / field_size
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elif pool_type == 'max':
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out[:, i, j, :] = np.max(x_masked, axis=(1, 2))
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return out
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class TestAdaptiveAvgPool2DAPI(unittest.TestCase):
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def setUp(self):
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self.x_np = np.random.random([2, 3, 7, 7]).astype("float32")
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self.res_1_np = adaptive_pool2d_forward(
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x=self.x_np, output_size=[3, 3], pool_type="avg"
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)
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self.res_2_np = adaptive_pool2d_forward(
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x=self.x_np, output_size=5, pool_type="avg"
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)
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self.res_3_np = adaptive_pool2d_forward(
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x=self.x_np, output_size=[2, 5], pool_type="avg"
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)
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self.res_4_np = adaptive_pool2d_forward(
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x=self.x_np, output_size=[3, 3], pool_type="avg", data_format="NHWC"
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)
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self.res_5_np = adaptive_pool2d_forward(
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x=self.x_np, output_size=[None, 3], pool_type="avg"
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)
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def test_static_graph(self):
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for use_cuda in (
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[False, True]
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if (core.is_compiled_with_cuda() or is_custom_device())
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else [False]
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):
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place = get_device_place() if use_cuda else paddle.CPUPlace()
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paddle.enable_static()
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(
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name="x", shape=[2, 3, 7, 7], dtype="float32"
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)
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out_1 = paddle.nn.functional.adaptive_avg_pool2d(
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x=x, output_size=[3, 3]
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)
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out_2 = paddle.nn.functional.adaptive_avg_pool2d(
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x=x, output_size=5
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)
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out_3 = paddle.nn.functional.adaptive_avg_pool2d(
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x=x, output_size=[2, 5]
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)
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out_4 = paddle.nn.functional.adaptive_avg_pool2d(
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x=x, output_size=[3, 3], data_format="NHWC"
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)
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out_5 = paddle.nn.functional.adaptive_avg_pool2d(
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x=x, output_size=[None, 3]
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)
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# test @param_one_alias(["x", "input"])
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out_6 = paddle.nn.functional.adaptive_avg_pool2d(
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input=x, output_size=[3, 3]
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)
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exe = paddle.static.Executor(place=place)
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[res_1, res_2, res_3, res_4, res_5, res_6] = exe.run(
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main_program,
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feed={"x": self.x_np},
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fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6],
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)
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np.testing.assert_allclose(
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res_1, self.res_1_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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res_2, self.res_2_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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res_3, self.res_3_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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res_4, self.res_4_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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res_5, self.res_5_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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res_6, self.res_1_np, rtol=1e-5, atol=1e-8
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)
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def test_dynamic_graph(self):
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for use_cuda in (
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[False, True]
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if (core.is_compiled_with_cuda() or is_custom_device())
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else [False]
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):
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place = get_device_place() if use_cuda else paddle.CPUPlace()
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paddle.disable_static(place=place)
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x = paddle.to_tensor(self.x_np)
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out_1 = paddle.nn.functional.adaptive_avg_pool2d(
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x=x, output_size=[3, 3]
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)
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out_2 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=5)
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out_3 = paddle.nn.functional.adaptive_avg_pool2d(
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x=x, output_size=[2, 5]
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)
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out_4 = paddle.nn.functional.adaptive_avg_pool2d(
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x=x, output_size=[3, 3], data_format="NHWC"
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)
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out_5 = paddle.nn.functional.adaptive_avg_pool2d(
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x=x, output_size=[None, 3]
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)
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out_6 = paddle.nn.functional.interpolate(
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x=x, mode="area", size=[2, 5]
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)
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out_7 = paddle.nn.functional.adaptive_avg_pool2d(
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input=x, output_size=[3, 3]
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)
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np.testing.assert_allclose(
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out_1.numpy(), self.res_1_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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out_2.numpy(), self.res_2_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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out_3.numpy(), self.res_3_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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out_4.numpy(), self.res_4_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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out_5.numpy(), self.res_5_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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out_6.numpy(), self.res_3_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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out_7.numpy(), self.res_1_np, rtol=1e-5, atol=1e-8
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)
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def test_grad(self):
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for use_cuda in (
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[False, True]
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if (core.is_compiled_with_cuda() or is_custom_device())
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else [False]
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):
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place = get_device_place() if use_cuda else paddle.CPUPlace()
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paddle.disable_static(place=place)
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x = paddle.to_tensor(self.x_np)
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x.stop_gradient = False
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for output_size in [[3, 3], [2, 5], [8, 8]]:
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out = paddle.nn.functional.adaptive_avg_pool2d(
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x=x, output_size=output_size
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)
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x_grad = paddle.grad(
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[out],
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[x],
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grad_outputs=paddle.ones_like(out),
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allow_unused=True,
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)
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np.testing.assert_allclose(
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paddle.sum(x_grad[0]), out.numel(), rtol=1e-6
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)
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class TestAdaptiveAvgPool2DClassAPI(unittest.TestCase):
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def setUp(self):
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self.x_np = np.random.random([2, 3, 7, 7]).astype("float32")
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self.res_1_np = adaptive_pool2d_forward(
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x=self.x_np, output_size=[3, 3], pool_type="avg"
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)
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self.res_2_np = adaptive_pool2d_forward(
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x=self.x_np, output_size=5, pool_type="avg"
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)
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self.res_3_np = adaptive_pool2d_forward(
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x=self.x_np, output_size=[2, 5], pool_type="avg"
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)
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self.res_4_np = adaptive_pool2d_forward(
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x=self.x_np, output_size=[3, 3], pool_type="avg", data_format="NHWC"
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)
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self.res_5_np = adaptive_pool2d_forward(
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x=self.x_np, output_size=[None, 3], pool_type="avg"
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)
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def test_static_graph(self):
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for use_cuda in (
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[False, True]
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if (core.is_compiled_with_cuda() or is_custom_device())
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else [False]
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):
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place = get_device_place() if use_cuda else paddle.CPUPlace()
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paddle.enable_static()
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(
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name="x", shape=[2, 3, 7, 7], dtype="float32"
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)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(
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output_size=[3, 3]
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)
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out_1 = adaptive_avg_pool(x=x)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=5)
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out_2 = adaptive_avg_pool(x=x)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(
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output_size=[2, 5]
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)
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out_3 = adaptive_avg_pool(x=x)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(
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output_size=[3, 3], data_format="NHWC"
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)
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out_4 = adaptive_avg_pool(x=x)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(
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output_size=[None, 3]
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)
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out_5 = adaptive_avg_pool(x=x)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(
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output_size=[3, 3]
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)
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out_6 = adaptive_avg_pool(input=x)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(
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output_size=[1, 3]
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)
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adaptive_avg_pool.output_size = [3, 3]
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out_7 = adaptive_avg_pool(input=x)
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exe = paddle.static.Executor(place=place)
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[res_1, res_2, res_3, res_4, res_5, res_6, res_7] = exe.run(
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main_program,
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feed={"x": self.x_np},
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fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7],
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)
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np.testing.assert_allclose(
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res_1, self.res_1_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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res_2, self.res_2_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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res_3, self.res_3_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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res_4, self.res_4_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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res_5, self.res_5_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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res_6, self.res_1_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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res_7, self.res_1_np, rtol=1e-5, atol=1e-8
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)
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def test_dynamic_graph(self):
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for use_cuda in (
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[False, True]
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if (core.is_compiled_with_cuda() or is_custom_device())
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else [False]
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):
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place = get_device_place() if use_cuda else paddle.CPUPlace()
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paddle.disable_static(place=place)
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x = paddle.to_tensor(self.x_np)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=[3, 3])
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out_1 = adaptive_avg_pool(x=x)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=5)
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out_2 = adaptive_avg_pool(x=x)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=[2, 5])
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out_3 = adaptive_avg_pool(x=x)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(
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output_size=[3, 3], data_format="NHWC"
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)
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out_4 = adaptive_avg_pool(x=x)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(
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output_size=[None, 3]
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)
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out_5 = adaptive_avg_pool(x=x)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=[3, 3])
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out_6 = adaptive_avg_pool(input=x)
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adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=[1, 3])
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adaptive_avg_pool.output_size = [3, 3]
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out_7 = adaptive_avg_pool(input=x)
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np.testing.assert_allclose(
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out_1.numpy(), self.res_1_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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out_2.numpy(), self.res_2_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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out_3.numpy(), self.res_3_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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out_4.numpy(), self.res_4_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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out_5.numpy(), self.res_5_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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out_6.numpy(), self.res_1_np, rtol=1e-5, atol=1e-8
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)
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np.testing.assert_allclose(
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out_7.numpy(), self.res_1_np, rtol=1e-5, atol=1e-8
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)
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class TestOutputSizeTensor(UnittestBase):
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def init_info(self):
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self.shapes = [[1, 3, 6, 6]]
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self.save_path = os.path.join(self.temp_dir.name, self.path_prefix())
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def test_static(self):
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paddle.enable_static()
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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fc = paddle.nn.Linear(6, 6)
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x = paddle.randn(self.shapes[0])
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x.stop_gradient = False
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feat = fc(x) # [1,3,6,6]
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out1, out2 = self.call_func(feat)
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sgd = paddle.optimizer.SGD()
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sgd.minimize(paddle.mean(out1 + out2))
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if not in_pir_mode():
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self.assertTrue(self.var_prefix() in str(main_prog))
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exe = paddle.static.Executor()
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exe.run(startup_prog)
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res = exe.run(fetch_list=[out1, out2])
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np.testing.assert_allclose(res[0], res[1])
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paddle.static.save_inference_model(
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self.save_path, [x], [out1, out2], exe
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)
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# Test for Inference Predictor
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infer_outs = self.infer_prog()
|
|
np.testing.assert_array_equal(infer_outs[0].shape, (1, 3, 3, 3))
|
|
np.testing.assert_allclose(infer_outs[0], infer_outs[1])
|
|
|
|
def path_prefix(self):
|
|
return 'pool2d_tensor'
|
|
|
|
def var_prefix(self):
|
|
return "Vars["
|
|
|
|
def call_func(self, x):
|
|
# list[Tensor]
|
|
output_size = [paddle.assign([3]), paddle.assign([3])]
|
|
out1 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=[3, 3])
|
|
out2 = paddle.nn.functional.adaptive_avg_pool2d(
|
|
x=x, output_size=output_size
|
|
)
|
|
return out1, out2
|
|
|
|
|
|
class TestOutputSizeListTensor(TestOutputSizeTensor):
|
|
def path_prefix(self):
|
|
return 'pool2d_tensors'
|
|
|
|
def call_func(self, x):
|
|
# list[int, Tensor]
|
|
output_size = [paddle.assign([3]), 3]
|
|
out1 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=[3, 3])
|
|
out2 = paddle.nn.functional.adaptive_avg_pool2d(
|
|
x=x, output_size=output_size
|
|
)
|
|
return out1, out2
|
|
|
|
|
|
class TestOutputSizeListTensor2(TestOutputSizeTensor):
|
|
def path_prefix(self):
|
|
return 'pool2d_tensor2'
|
|
|
|
def call_func(self, x):
|
|
# A Tensor
|
|
output_size = paddle.assign([3, 3])
|
|
out1 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=[3, 3])
|
|
out2 = paddle.nn.functional.adaptive_avg_pool2d(
|
|
x=x, output_size=output_size
|
|
)
|
|
return out1, out2
|
|
|
|
|
|
class TestAdaptiveAvgPool2DAPI_ZeroSize(unittest.TestCase):
|
|
def setUp(self):
|
|
self.x_np = np.random.random([0, 3, 7, 7]).astype("float32")
|
|
self.res_1_np = adaptive_pool2d_forward(
|
|
x=self.x_np, output_size=[3, 3], pool_type="avg"
|
|
)
|
|
|
|
def test_static_graph(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
paddle.enable_static()
|
|
|
|
main_program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
|
|
with paddle.static.program_guard(main_program, startup_program):
|
|
x = paddle.static.data(
|
|
name="x", shape=[0, 3, 7, 7], dtype="float32"
|
|
)
|
|
|
|
out_1 = paddle.nn.functional.adaptive_avg_pool2d(
|
|
x=x, output_size=[3, 3]
|
|
)
|
|
|
|
exe = paddle.static.Executor(place=place)
|
|
[res_1] = exe.run(
|
|
main_program,
|
|
feed={"x": self.x_np},
|
|
fetch_list=[out_1],
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
res_1, self.res_1_np, rtol=1e-5, atol=1e-8
|
|
)
|
|
|
|
def test_dynamic_graph(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
paddle.disable_static(place=place)
|
|
x = paddle.to_tensor(self.x_np)
|
|
|
|
out_1 = paddle.nn.functional.adaptive_avg_pool2d(
|
|
x=x, output_size=[3, 3]
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
out_1.numpy(), self.res_1_np, rtol=1e-5, atol=1e-8
|
|
)
|
|
|
|
def test_grad(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
paddle.disable_static(place=place)
|
|
x = paddle.to_tensor(self.x_np)
|
|
x.stop_gradient = False
|
|
|
|
out_1 = paddle.nn.functional.adaptive_avg_pool2d(
|
|
x=x, output_size=[3, 3]
|
|
)
|
|
loss = paddle.sum(out_1)
|
|
loss.backward()
|
|
np.testing.assert_allclose(x.grad.shape, x.shape)
|
|
|
|
|
|
class TestInterpolateAPI_ZeroSize(unittest.TestCase):
|
|
def setUp(self):
|
|
self.x_np = np.random.random([0, 3, 7, 7]).astype("float32")
|
|
|
|
def test_functional_interpolate(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
paddle.disable_static(place=place)
|
|
x = paddle.to_tensor(self.x_np)
|
|
x.stop_gradient = False
|
|
|
|
out = paddle.nn.functional.interpolate(
|
|
x=x, mode="area", size=[2, 5]
|
|
)
|
|
res_np = adaptive_pool2d_forward(
|
|
x=self.x_np, output_size=[2, 5], pool_type="avg"
|
|
)
|
|
np.testing.assert_allclose(
|
|
out.numpy(), res_np, rtol=1e-5, atol=1e-8
|
|
)
|
|
|
|
loss = paddle.sum(out)
|
|
loss.backward()
|
|
np.testing.assert_allclose(x.grad.shape, x.shape)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|