wehub-resource-sync
685 lines
23 KiB
Python
685 lines
23 KiB
Python
# Copyright (c) 2023 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 (
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check_out_dtype,
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get_device,
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get_device_place,
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is_custom_device,
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)
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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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from paddle.base import core
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def fractional_rational_u(u, alpha, input, output, pool_size=0):
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if pool_size > 0:
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return u
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base = input // output
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u_max1 = (base + 2) / alpha - 1
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u_max2 = (input + 1 - base) / alpha - (output - 1)
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max_u = min(u_max1, u_max2)
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return u * max_u
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def fractional_start_index(idx, alpha, u, pool_size=0):
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return int((idx + u) * alpha) - int(u * alpha)
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def fractional_end_index(idx, alpha, u, pool_size=0):
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if pool_size > 0:
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return int((idx + u) * alpha) - int(u * alpha) + pool_size
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return int((idx + 1 + u) * alpha) - int(u * alpha)
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def fractional_pool2d_forward(
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x,
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output_size,
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kernel_size=None,
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random_u=None,
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data_format='NCHW',
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pool_type="max",
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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 kernel_size is None:
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pool_height = 0
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pool_width = 0
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elif isinstance(kernel_size, int):
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pool_height = kernel_size
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pool_width = kernel_size
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else:
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pool_height, pool_width = kernel_size
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if isinstance(output_size, int):
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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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u = random_u
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alpha_height = (H - pool_height) / (H_out - (1 if pool_height > 0 else 0))
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alpha_width = (W - pool_width) / (W_out - (1 if pool_width > 0 else 0))
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u_height = fractional_rational_u(u, alpha_height, H, H_out, pool_height)
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u_width = fractional_rational_u(u, alpha_width, W, W_out, pool_width)
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for i in range(H_out):
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h_start = fractional_start_index(i, alpha_height, u_height, pool_height)
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h_end = fractional_end_index(i, alpha_height, u_height, pool_height)
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h_start = max(h_start, 0)
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h_end = min(h_end, H)
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for j in range(W_out):
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w_start = fractional_start_index(
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j, alpha_width, u_width, pool_width
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)
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w_end = fractional_end_index(j, alpha_width, u_width, pool_width)
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w_start = max(w_start, 0)
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w_end = min(w_end, W)
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if data_format == 'NCHW':
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x_masked = x[:, :, h_start:h_end, w_start:w_end]
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if pool_type == 'avg':
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field_size = (h_end - h_start) * (w_end - w_start)
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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[:, h_start:h_end, w_start:w_end, :]
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if pool_type == 'avg':
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field_size = (h_end - h_start) * (w_end - w_start)
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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 TestFractionalMaxPool2DAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(2023)
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self.x_np = np.random.random([2, 3, 7, 7]).astype("float32")
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self.res_1_np = fractional_pool2d_forward(
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x=self.x_np, output_size=[3, 3], random_u=0.3
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)
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self.res_2_np = fractional_pool2d_forward(
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x=self.x_np, output_size=5, random_u=0.5
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)
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self.res_3_np = fractional_pool2d_forward(
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x=self.x_np, output_size=[2, 5], random_u=0.7
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)
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self.res_4_np = fractional_pool2d_forward(
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x=self.x_np, kernel_size=2, output_size=[3, 3], random_u=0.6
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)
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self.res_5_np = fractional_pool2d_forward(
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x=self.x_np, kernel_size=[2, 2], output_size=[3, 3], random_u=0.6
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)
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self.res_6_np = fractional_pool2d_forward(
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x=self.x_np, output_size=[None, 3], random_u=0.6
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)
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self.res_7_np = fractional_pool2d_forward(
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x=self.x_np, output_size=[3, None], random_u=0.6
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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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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.fractional_max_pool2d(
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x=x, output_size=[3, 3], random_u=0.3
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)
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out_2 = paddle.nn.functional.fractional_max_pool2d(
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x=x, output_size=5, random_u=0.5
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)
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out_3 = paddle.nn.functional.fractional_max_pool2d(
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x=x, output_size=[2, 5], random_u=0.7
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)
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out_4 = paddle.nn.functional.fractional_max_pool2d(
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x=x, kernel_size=2, output_size=[3, 3], random_u=0.6
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)
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out_5 = paddle.nn.functional.fractional_max_pool2d(
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x=x, kernel_size=[2, 2], output_size=[3, 3], random_u=0.6
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)
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out_6 = paddle.nn.functional.fractional_max_pool2d(
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x=x, output_size=[None, 3], random_u=0.6
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)
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out_7 = paddle.nn.functional.fractional_max_pool2d(
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x=x, output_size=[3, None], random_u=0.6
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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, res_7] = exe.run(
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base.default_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(res_1, self.res_1_np)
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np.testing.assert_allclose(res_2, self.res_2_np)
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np.testing.assert_allclose(res_3, self.res_3_np)
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np.testing.assert_allclose(res_4, self.res_4_np)
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np.testing.assert_allclose(res_5, self.res_5_np)
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np.testing.assert_allclose(res_6, self.res_6_np)
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np.testing.assert_allclose(res_7, self.res_7_np)
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def test_static_graph_return_mask(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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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.fractional_max_pool2d(
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x=x, output_size=[3, 3], return_mask=True, random_u=0.3
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)
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out_2 = paddle.nn.functional.fractional_max_pool2d(
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x=x, output_size=5, return_mask=True, random_u=0.5
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)
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out_3 = paddle.nn.functional.fractional_max_pool2d(
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x=x, output_size=[2, 5], return_mask=True, random_u=0.7
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)
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out_4 = paddle.nn.functional.fractional_max_pool2d(
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x=x,
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kernel_size=2,
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output_size=[3, 3],
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return_mask=True,
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random_u=0.6,
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)
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out_5 = paddle.nn.functional.fractional_max_pool2d(
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x=x,
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kernel_size=[2, 2],
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output_size=[3, 3],
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return_mask=True,
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random_u=0.6,
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)
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out_6 = paddle.nn.functional.fractional_max_pool2d(
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x=x, output_size=[None, 3], return_mask=True, random_u=0.6
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)
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out_7 = paddle.nn.functional.fractional_max_pool2d(
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x=x, output_size=[3, None], return_mask=True, random_u=0.6
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)
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exe = paddle.static.Executor(place=place)
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[
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res_1,
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mask_1,
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res_2,
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mask_2,
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res_3,
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mask_3,
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res_4,
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mask_4,
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res_5,
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mask_5,
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res_6,
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mask_6,
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res_7,
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mask_7,
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] = exe.run(
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base.default_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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self.assertEqual(res_1.shape, mask_1.shape)
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self.assertEqual(res_2.shape, mask_2.shape)
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self.assertEqual(res_3.shape, mask_3.shape)
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self.assertEqual(res_4.shape, mask_4.shape)
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self.assertEqual(res_5.shape, mask_5.shape)
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self.assertEqual(res_6.shape, mask_6.shape)
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self.assertEqual(res_7.shape, mask_7.shape)
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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, device = (
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(get_device_place(), get_device())
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if use_cuda
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else (paddle.CPUPlace(), 'cpu')
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)
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paddle.disable_static(place=place)
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paddle.set_device(device)
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x = paddle.to_tensor(self.x_np)
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out_1 = paddle.nn.functional.fractional_max_pool2d(
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x=x, return_mask=False, output_size=[3, 3], random_u=0.3
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)
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out_2 = paddle.nn.functional.fractional_max_pool2d(
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x=x, output_size=5, random_u=0.5
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)
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out_3 = paddle.nn.functional.fractional_max_pool2d(
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x=x, output_size=[2, 5], random_u=0.7
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)
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out_4 = paddle.nn.functional.fractional_max_pool2d(
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x=x, kernel_size=2, output_size=[3, 3], random_u=0.6
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)
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out_5 = paddle.nn.functional.fractional_max_pool2d(
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x=x, kernel_size=[2, 2], output_size=[3, 3], random_u=0.6
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)
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out_6 = paddle.nn.functional.fractional_max_pool2d(
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x=x, output_size=[None, 3], random_u=0.6
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)
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out_7 = paddle.nn.functional.fractional_max_pool2d(
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x=x, output_size=[3, None], random_u=0.6
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)
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# test param_two_alias(["x", "input"], ["return_mask", "return_indices"])
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out_8 = paddle.nn.functional.fractional_max_pool2d(
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input=x,
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output_size=[3, None],
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random_u=0.6,
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return_indices=False,
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)
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np.testing.assert_allclose(out_1.numpy(), self.res_1_np)
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np.testing.assert_allclose(out_2.numpy(), self.res_2_np)
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np.testing.assert_allclose(out_3.numpy(), self.res_3_np)
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np.testing.assert_allclose(out_4.numpy(), self.res_4_np)
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np.testing.assert_allclose(out_5.numpy(), self.res_5_np)
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np.testing.assert_allclose(out_6.numpy(), self.res_6_np)
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np.testing.assert_allclose(out_7.numpy(), self.res_7_np)
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np.testing.assert_allclose(out_8.numpy(), self.res_7_np)
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class TestFractionalMaxPool2DClassAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(2023)
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self.x_np = np.random.random([2, 3, 7, 7]).astype("float32")
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self.res_1_np = fractional_pool2d_forward(
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x=self.x_np, output_size=[3, 3], random_u=0.3
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)
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self.res_2_np = fractional_pool2d_forward(
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x=self.x_np, output_size=5, random_u=0.5
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)
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self.res_3_np = fractional_pool2d_forward(
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x=self.x_np, output_size=[2, 5], random_u=0.7
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)
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self.res_4_np = fractional_pool2d_forward(
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x=self.x_np, kernel_size=2, output_size=[3, 3], random_u=0.6
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)
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self.res_5_np = fractional_pool2d_forward(
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x=self.x_np, kernel_size=[2, 2], output_size=[3, 3], random_u=0.6
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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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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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fractional_max_pool = paddle.nn.FractionalMaxPool2D(
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output_size=[3, 3], random_u=0.3
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)
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out_1 = fractional_max_pool(x=x)
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fractional_max_pool = paddle.nn.FractionalMaxPool2D(
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output_size=5, random_u=0.5
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)
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out_2 = fractional_max_pool(x=x)
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fractional_max_pool = paddle.nn.FractionalMaxPool2D(
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output_size=[2, 5], random_u=0.7
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)
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out_3 = fractional_max_pool(x=x)
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fractional_max_pool = paddle.nn.FractionalMaxPool2D(
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kernel_size=2, output_size=[3, 3], random_u=0.6
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)
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out_4 = fractional_max_pool(x=x)
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fractional_max_pool = paddle.nn.FractionalMaxPool2D(
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kernel_size=[2, 2], output_size=[3, 3], random_u=0.6
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)
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out_5 = fractional_max_pool(x=x)
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exe = paddle.static.Executor(place=place)
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res = exe.run(
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base.default_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],
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)
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np.testing.assert_allclose(res[0], self.res_1_np)
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np.testing.assert_allclose(res[1], self.res_2_np)
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np.testing.assert_allclose(res[2], self.res_3_np)
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np.testing.assert_allclose(res[3], self.res_4_np)
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np.testing.assert_allclose(res[4], self.res_5_np)
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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, device = (
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(get_device_place(), get_device())
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if use_cuda
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else (paddle.CPUPlace(), 'cpu')
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)
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paddle.disable_static(place=place)
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paddle.set_device(device)
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x = paddle.to_tensor(self.x_np)
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fractional_max_pool = paddle.nn.FractionalMaxPool2D(
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output_size=[3, 3], random_u=0.3
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)
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out_1 = fractional_max_pool(x=x)
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fractional_max_pool = paddle.nn.FractionalMaxPool2D(
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output_size=5, random_u=0.5
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)
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out_2 = fractional_max_pool(x=x)
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fractional_max_pool = paddle.nn.FractionalMaxPool2D(
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output_size=[2, 5], random_u=0.7
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)
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out_3 = fractional_max_pool(x=x)
|
|
|
|
fractional_max_pool = paddle.nn.FractionalMaxPool2D(
|
|
kernel_size=2, output_size=[3, 3], random_u=0.6
|
|
)
|
|
out_4 = fractional_max_pool(x=x)
|
|
|
|
fractional_max_pool = paddle.nn.FractionalMaxPool2D(
|
|
kernel_size=[2, 2], output_size=[3, 3], random_u=0.6
|
|
)
|
|
out_5 = fractional_max_pool(x=x)
|
|
|
|
# test param_one_alias(["x", "input"])
|
|
fractional_max_pool = paddle.nn.FractionalMaxPool2D(
|
|
kernel_size=[2, 2], output_size=[3, 3], random_u=0.6
|
|
)
|
|
out_6 = fractional_max_pool(input=x)
|
|
|
|
# test param_one_alias(["return_mask", "return_indices"])
|
|
fractional_max_pool = paddle.nn.FractionalMaxPool2D(
|
|
kernel_size=[2, 2],
|
|
output_size=[3, 3],
|
|
random_u=0.6,
|
|
return_indices=False,
|
|
)
|
|
out_7 = fractional_max_pool(x=x)
|
|
|
|
np.testing.assert_allclose(out_1.numpy(), self.res_1_np)
|
|
np.testing.assert_allclose(out_2.numpy(), self.res_2_np)
|
|
np.testing.assert_allclose(out_3.numpy(), self.res_3_np)
|
|
np.testing.assert_allclose(out_4.numpy(), self.res_4_np)
|
|
np.testing.assert_allclose(out_5.numpy(), self.res_5_np)
|
|
np.testing.assert_allclose(out_6.numpy(), self.res_5_np)
|
|
np.testing.assert_allclose(out_7.numpy(), self.res_5_np)
|
|
|
|
|
|
class TestOutDtype(unittest.TestCase):
|
|
def test_max_pool(self):
|
|
api_fn = F.fractional_max_pool2d
|
|
shape = [1, 3, 32, 32]
|
|
check_out_dtype(
|
|
api_fn,
|
|
in_specs=[(shape,)],
|
|
expect_dtypes=['uint16', 'float16', 'float32', 'float64'],
|
|
output_size=16,
|
|
)
|
|
|
|
|
|
class TestFractionalMaxPool2DAPIDtype(unittest.TestCase):
|
|
def test_dtypes(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place, device = (
|
|
(get_device_place(), get_device())
|
|
if use_cuda
|
|
else (paddle.CPUPlace(), 'cpu')
|
|
)
|
|
paddle.disable_static(place=place)
|
|
paddle.set_device(device)
|
|
|
|
dtypes = ['float32', 'float64']
|
|
|
|
if core.is_float16_supported(place):
|
|
dtypes += ['float16']
|
|
|
|
if use_cuda and core.is_bfloat16_supported(place):
|
|
dtypes += ['uint16']
|
|
|
|
for dtype in dtypes:
|
|
np.random.seed(2023)
|
|
x_np = np.random.random([2, 3, 7, 7]).astype(dtype)
|
|
res_np = fractional_pool2d_forward(
|
|
x=x_np, output_size=[3, 3], random_u=0.3
|
|
)
|
|
|
|
x_paddle = paddle.to_tensor(x_np)
|
|
out = paddle.nn.functional.fractional_max_pool2d(
|
|
x=x_paddle, output_size=[3, 3], random_u=0.3
|
|
)
|
|
|
|
np.testing.assert_allclose(out.numpy(), res_np)
|
|
|
|
|
|
class TestFractionalMaxPool2DAPIRandomU(unittest.TestCase):
|
|
def test_none_random_u(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place, device = (
|
|
(get_device_place(), get_device())
|
|
if use_cuda
|
|
else (paddle.CPUPlace(), 'cpu')
|
|
)
|
|
paddle.disable_static(place=place)
|
|
paddle.set_device(device)
|
|
|
|
np.random.seed(2023)
|
|
x_np = paddle.to_tensor(np.random.random([2, 3, 7, 7]))
|
|
|
|
res_np = paddle.nn.functional.fractional_max_pool2d(
|
|
x=x_np, output_size=[3, 3], random_u=None
|
|
)
|
|
|
|
self.assertTrue(list(res_np.shape) == [2, 3, 3, 3])
|
|
|
|
def test_error_random_u(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place, device = (
|
|
(get_device_place(), get_device())
|
|
if use_cuda
|
|
else (paddle.CPUPlace(), 'cpu')
|
|
)
|
|
paddle.disable_static(place=place)
|
|
paddle.set_device(device)
|
|
|
|
np.random.seed(2023)
|
|
x_np = np.random.random([2, 3, 7, 7])
|
|
|
|
# error random_u of `<0`
|
|
with self.assertRaises(ValueError):
|
|
res_np = paddle.nn.functional.fractional_max_pool2d(
|
|
x=x_np, output_size=[3, 3], random_u=-0.2
|
|
)
|
|
|
|
# error random_u of `0`
|
|
with self.assertRaises(ValueError):
|
|
res_np = paddle.nn.functional.fractional_max_pool2d(
|
|
x=x_np, output_size=[3, 3], random_u=0
|
|
)
|
|
|
|
# error random_u of `1`
|
|
with self.assertRaises(ValueError):
|
|
res_np = paddle.nn.functional.fractional_max_pool2d(
|
|
x=x_np, output_size=[3, 3], random_u=1
|
|
)
|
|
|
|
# error random_u of `>1`
|
|
with self.assertRaises(ValueError):
|
|
res_np = paddle.nn.functional.fractional_max_pool2d(
|
|
x=x_np, output_size=[3, 3], random_u=1.2
|
|
)
|
|
|
|
|
|
class TestFractionalMaxPool2DAPIErrorOutputSize(unittest.TestCase):
|
|
def test_error_output_size(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place, device = (
|
|
(get_device_place(), get_device())
|
|
if use_cuda
|
|
else (paddle.CPUPlace(), 'cpu')
|
|
)
|
|
paddle.disable_static(place=place)
|
|
paddle.set_device(device)
|
|
|
|
np.random.seed(2023)
|
|
x_np = np.random.random([2, 3, 7, 7])
|
|
|
|
with self.assertRaises(ValueError):
|
|
res_np = paddle.nn.functional.fractional_max_pool2d(
|
|
x=x_np, output_size=[7, 7], random_u=0.2
|
|
)
|
|
|
|
with self.assertRaises(ValueError):
|
|
res_np = paddle.nn.functional.fractional_max_pool2d(
|
|
x=x_np, kernel_size=2, output_size=[6, 6], random_u=0.2
|
|
)
|
|
|
|
|
|
class TestFractionalMaxPool2DAPI_ZeroSize(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(2023)
|
|
self.x_np = np.random.random([2, 0, 7, 7]).astype("float32")
|
|
self.res_1_np = fractional_pool2d_forward(
|
|
x=self.x_np, output_size=[3, 3], random_u=0.3
|
|
)
|
|
|
|
def test_dynamic_graph(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place, device = (
|
|
(get_device_place(), get_device())
|
|
if use_cuda
|
|
else (paddle.CPUPlace(), 'cpu')
|
|
)
|
|
paddle.disable_static(place=place)
|
|
paddle.set_device(device)
|
|
|
|
x = paddle.to_tensor(self.x_np)
|
|
x.stop_gradient = False
|
|
|
|
out_1 = paddle.nn.functional.fractional_max_pool2d(
|
|
x=x, return_mask=False, output_size=[3, 3], random_u=0.3
|
|
)
|
|
|
|
np.testing.assert_allclose(out_1.numpy(), self.res_1_np)
|
|
loss = paddle.sum(out_1)
|
|
loss.backward()
|
|
np.testing.assert_allclose(x.grad.shape, x.shape)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|